ANN training the analysis of the selected procedures in Matlab environment
|
|
- Roberta Simon
- 6 years ago
- Views:
Transcription
1 ANN training the analysis of the selected procedures in Matlab environment Jacek Bartman, Zbigniew Gomółka, Bogusław Twaróg University of Rzeszow, Department of Computer Engineering, Rzeszow, Pigonia 1, Poland s: {jbartman, zgomolka, Abstract. The article presents the development of artificial neural networks in Matlab environment. It comprises description of information that is stored in the variable representing the neural network and analysis of the function used to learn artificial neural networks. Keywords: artificial intelligence, neural network, matlab, ANN training 1 Introduction Matlab is a programming environment dedicated primarily to calculations and computer simulations; of course, it can be applied in other fields as well. The main component of the environment is a command interpreter that lets you work in batch mode and interactive mode - by issuing single commands in the command line. An integral, but optional part of Matlab are libraries (so called toolboxes) representing a set of m-filesthat are dedicated for the applications in a narrow specialty, e.g.: NNet groups the functions in the field of Artificial Neural Networks, Fuzzy in the field of fuzzy sets. Some libraries need to be installed before other libraries,as they use the functions that are contained in the former. Simplicity, intuitiveness and a graphical presentation of results makes Matlab a tool which is very often applied. Extended thematic libraries facilitate the development of programs, as it happens, e.g. in case of NNet library which is dedicated to artificial neural networks. A programmer, by selecting parameters will virtually have an impact on every single element of the proposed neural network: it establishes its architecture, activation function of neurons, training method together with its parameters, the method of assessing the progress of training, selects a trainingset determining its division into training, testing and validating subsets. This means that Matlab is very flexible for its users as they can customized it to their own needs. The apparent disadvantage of the package is a great quantity of service functions - it makes it difficult to create universal and unified programs. While it is very easy to construct functions for a particular task in Matlab, it is very complicated to create features that would fully attune to the philosophy of the package, be able to use its full capabilities and behave the same as the original functions. A huge amount of induced functions with different parameters, whose names and configuration change is the main difficulty here. 88
2 2 Matlab creating multilayer feedforward neural networks For creating artificial neural networks the package offers a few commands which are [2, 3]: newff - creating a multilayer feedforward neural network, newfffd - creating a multilayer feedforward neural network with a time delay vector, newp - creating a single layer network consisting of preceptors newlin - creating a single layer network consisting of linear neurons. newlind - designing a single layer network consisting of linear neurons. Before creating the network it is necessary to define the matrix of: a training set of data P = [ ; ]; a set of expected data T = [ ]; With so defined sets of data we can create, at a later stage, a variable net (you can specify any other name) representing the neural network. In this variable, which is formally a structure, all the information about the construction of the network created is stored. For constructing the network a newff.m function creating a multilayer feedforward neural network will be used. The syntax of this function is as follows: net = newff(p,t,s,tf,btf,blf,pf,ipf,opf,ddf) where: P aset of training data; T aset of expected results; Si an amount of neurons in particular hidden layers, hence the index i ; TFi names of the activation function for particular layers. A default activation function for the hidden layers is a tangensoidal function, and linear function (pureline) for the output layer; BTF a name of the training network method, Levenberga-Marquardta (trainlm) algorithm by default; BLF the name of function used for the modification of weights, learngdm by default PF goal function,the mean squared error (mse) by default; IPF table row cells of the input processing functions which are default: fixunknowns, removeconstantrows, mapminmax; OPF table row cells of the output processing functions which are default: removeconstantrows, mapminmax; DDF the functions of training data set division to the set of relevant, validating and testing data, dividerand.m by default; net the created artificial neural network. 89
3 2.1 The formula of the neural network structure A representative network variable net contains thorough information on the architecture of the created neural network.the values of basic network parameters can be obtained by inserting a name variable (e.g. net) directly in the command line: net = Neural Network object: architecture: numinputs: 1 numlayers: 2 biasconnect: [1; 1] inputconnect: [1; 0] layerconnect: [0 0; 1 0] outputconnect: [0 1] numoutputs: 1 (read-only) numinputdelays: 0 (read-only) numlayerdelays: 0 (read-only) a number of network inputs a number of network layers subobject structures: inputs: {1x1 cell} of inputs layers: {2x1 cell} of layers outputs: {1x2 cell} containing 1 output biases: {2x1 cell} containing 2 biases inputweights: {2x1 cell} containing 1 input weight layerweights: {2x2 cell} containing 1 layer weight functions: adaptfcn: 'trains' dividefcn: 'dividerand' gradientfcn: 'calcgrad' initfcn: 'initlay' performfcn: 'mse' plotfcns:{'plotperform','plottrainstate','plotregression'} trainfcn: 'traingd' parameters: adaptparam:.passes divideparam:.trainratio,.valratio,.testratio gradientparam: (none) initparam: (none) performparam: (none) Defines a part of data set used for: - relevant training - trainratio (default 60%) - validation- valratio (default20%) - tests - test Ratio (default20%) 90
4 trainparam:.show,.showwindow,.showcommandline,.epochs,.time,.goal,.max_fail,.lr,.min_grad, the number of epochs that the results are shown graph. training presentation (nntraintool.m) generating output of command line max.number of training epochs max. time of training network goal function max. number of error changes training rate minimal change of gradient weight and bias values: IW: {2x1 cell} containing 1 input weight matrix LW: {2x2 cell} containing 1 layer weight matrix b: {2x1 cell} containing 2 bias vectors other: name: '' userdata: (user information) The values of particular parameters can be changed. To do so, one must substitute a new value in the right field. For example, if we want to change the maximum number of training to 1000, we need to give the following command [1]: net.trainparam.epochs=1000 Apart from the basic parameters in the net object structure, the hidden details of the network construction are saved as well.to obtain information about them, we need to give the following command: net.hint Then a list of elements appears, which are mostly the complex structures from which we can learn about, e.g. the size of the network input layer (net.hint.inputsizes), the size of the network output layer (net.hint.outputsizes), the transition functions that are used in particular layers (net.hint.transferfcn), indexation of synaptic weights in the input layer(net.hint.inputweightind{i})and in further layers (net.hint.layerweightind{i,j}), and indexation of biases (net.hint.biasind{i}), the number of all the weights and biases values (net.hint.xlen). 91
5 3 MATLAB neural network training The created neural network contains random values of weights and biases. The training can be achieved by using the train or adapt functions. The train function trains the neural network according to the selected training method that is included in the net.trainfcn field, and parameters included in the fields net.trainparam (the adapt function uses analogic fields net.adaptfcn and net.adaptparam). The basic difference between both functions is that the adapt function does only one training epoch, while the train function learns until one of the stop conditions are acquired [4]: the error will be achieved in the defined net.trainparam.goal field, the maximum number of training epochs will be exceeded, given in the olunet.trainparam.epochs field, the time for training the network will exceed the value defined in the net.trainparam.time field, othercondition will be achieved, which results from the specification of the method which is used for training. The syntax of both commands is equivalent: [ne ttr Y E] = train(net,p,t); The input parameters are: the learnt network (net), the matrix of input vectors (P), the matrix of expected answers (T). The function returns the learnt network (net), and the process of training (tr), the values of network answers (Y), training errors (E). 3.1 Theoretical basics of the selected method of training(of classic backward method of error propagation) A basic training method of feedforward multilayer neural networks is the method of backward propagation of error that for defining the weights correction uses the error gradient: where: E goal function (mean squared error); η training rate; w kj value j-of this weight k-of this neuron. When we assume that the correction of weights comes after inserting all training elements, the mean squared error, which describes the goal function, will look as follows: m 1 out E = ( d ) 2 k yk 2 k = 1 92
6 where: m the number of neurons in the output layer; d k the value of the expected answer k of this neuron; y k virtual answer k-of this neuron. When we include the dependencies of network architecture and the properties of variables, we obtain a formula for the neurons weights correction. In case of the output layer it looks as follows: And for the hidden layers: m out h ( f ) h out d u d u k out j in ji η ( k k ) out kj 1 i k = 1 du k du j f ) ( w = d y w x where: f transition function of neurons (activation); d k the value of the expected answer k of this neuron; y k virtual answer k-of this neuron. 3.2 The analysis of the method implementation The presented dependencies in part 3.1 were implemented in Matlab in the traingd function. In Matlab there are also other implementations of training methods, for each a separate function is dedicated. All training functions have a common prefix - train*. In the basic version the backward method of error propagation is quite slow, however its implementation contains all components that are characteristic for training neural networks in Matlab. The script begins with a function subject line which looks as follows: function[net,tr]=traingd(net,tr,trainv,valv,testv,varargin) where: net the object which describes the architecture of neural network (initiated by train.m); tr the parameter which contains the description of the training process, (initiated by the train.m function); trainv the training set(created by the train.m function); valv the validating set (created by the train.m function); testv the testing set (created by the train.m function); varagin the optional argument, allows receiving a varied amount of data. Below the subject line there is a description of function; it appears after giving the help traingd command. The comment includes the information about the 93
7 formal parameters of functions, gives default values of network parameters, determined during its creation and marks the training algorithm. The working part of the traingd function was divided into sections where each is responsible for a particular task. The more extended sections were divided into blocks. The names of sections are preceded with the %% symbol and the names of blocks are preceded with the comment symbol %. Below there is a characterisation of other sections that are included in the traingdfunction. Info section The Info section contains basic information about the training method. The section content may be viewed by issuing the command: traingd('info') We will receive the answer: ans = function: 'traingd' title: 'Gradient Descent Backpropagation' type: 'Training' version: 6 training_mode: 'Supervised' gradient_mode: 'Gradient' uses_validation: 1 param_defaults: [1x1 struct] training_states: [1x2 struct] giving, among others, a file name - 'traingd', method name - 'Gradient Descent Backpropagation', training mode - 'Supervised'. The block is also used to assign default values of network parameters to fields info.param_defaults.* NNET section 5.1 Backward Compatibility Another section s name is NNET 5.1 Backward Compatibility is responsible for the compatibility withthe previous versions of functions. The Parameters block, which is included in the section, creates variables where the training network parameters are stored: % Parameters epochs = net.trainparam.epochs; goal = net.trainparam.goal; lr = net.trainparam.lr; max_fail = net.trainparam.max_fail; min_grad = net.trainparam.min_grad; show = net.trainparam.show; 94
8 time = net.trainparam.time; gradientfcn = net.gradientfcn; Defined Variables improve the clarity of functions and reduce the size of its code. Another element of NNET section 5.1 Backward Compatibility is the Parameter Checking block; it checks whether the values passed to the function of training parameters (stored in the transferred network net) are acceptable or whether they are commonsense. Here's an example condition which checks whether the value of a variable describing the maximum number of training epochs is correct: if (~isa(epochs,'double')) (~isreal(epochs))... (any(size(epochs)) ~= 1) (epochs < 1)... (round(epochs) ~= epochs) error('nnet:arguments','epochs is not a positive integer.') end The instruction consecutively checks whether the epochs variable: is not the floating type of double precision ~isa(epochs,'double'), has no value that belongs to real numbers ~isreal(epochs), is the matrix jestany(size(epochs)) ~= 1, has a value lower than 1 epochs < 1, has no incomplete value round(epochs) ~= epochs. When any of these conditions is fulfilled, an error message is displayed and training is not performed. Similarly, other training parameters are tested. The last two blocks of the section is are Initialize and Initialize Performance. The first one, Initalize, initiates five new variables: % Initialize Q = trainv.q; TS = trainv.ts; val_fail = 0; starttime = clock; X = getx(net); TrainV is one of the parameters passed to the trangd function; it contains information about the data that is used for network training (about the data for which the appropriate training is performed). The Q field indicates the number of training vectors, and the field TS informs about the number of time steps. The Val_fail variable is used to count the number of error steps of training, and the starttime variable saves the time of the start of training neural network. To initiate the starttime variable the clock built-in function is used, returning a six-element data vector that contains a current date and time in the form of: [year month day hour minute second ]. The last of the initiated variables ( X ) is used to save the initial values of weights and biases. They are obtained by using the getx(net) function. Each weight and bias of network has assigned index that can be read from hidden network parameters: 95
9 net.hint.inputweightind indices of input synaptic weights; net.hint.layerweightind indices of layered synaptic weights; net.hint.biasind indices for threshold values. Current values of weights and biases can be viewed after inserting: net.iw{i} current values for input synaptic weights. Letter iin brackets substitutes the numer of layer about which a user wants to display the current weights values; net.lw{i} current values for layered synaptic weights; net.b{i} current values for biases. The number of included synaptic weights and biases is stored by: net.hint.xlen.net object field. The following block is responsible for assigning weights and biases to a particular index: x = zeros(net.hint.xlen,1); for i=1:net.numlayers for j=find(inputlearn(i,:)) x(inputweightind{i,j}) = net.iw{i,j}(:); end for j=find(layerlearn(i,:)) x(layerweightind{i,j}) = net.lw{i,j}(:); end if biaslearn(i) x(biasind{i}) = net.b{i}; end end The block begins with a command of creating a zero matrix The value of net.hin.xlen has influence on the dimension of the zero matrix. It specifies how many lines the matrix will contain. At a later stage the zero matrix will be replaced by other values than zero. In the next line the for. loop starts. The number of its iterations represents the number of layers of neural network. It contains twofor loopsand one conditional instruction. The for j=find(inputlearn(i, :) loop will be executed as long as the value of the inputlearn field value will equal 1. According to the appropriate indexation, the value net.iw {i, j} (:) will be assigned to the X vector. The second loop operates in ananalog manner. And the ifbiaslearn(i) conditional instruction is responsible for entering threshold values in appropriate indices. The second initiating block is the Initialize Performance block that initiates the variables used to assess network performance. The calcperf2 function, which is created in it, sets the initial values of the (perf) goal function, errors(ei), output values(trainv.y). 96
10 [perf,el,trainv.y,ac,n,zb,zi,zl] = calcperf2(net,x,trainv.pd,trainv.tl,trainv.ai,q,ts); The above initiation of functions, here, leads to the determining the initial values of goal function (perf), errors (El), output values (trainv.y), using the following arguments: net the already known net object, for the function used, among others, the parameters like: number of layers net.numlayers or the selected goal function (e.g. mse or sse) net.performfcn; X current values of synaptic weights and biases saved in the form of a singular vector, created by using the getx(net) function; trainv.pd the matrix of the delays of input signal samples in network; trainv.tl stores a set of expected values; trainv.ai the matrix of the delays of signal samples in the following network layers; Q A drawn number of learning vectors, in the dividerand.m function, for which the right training (trainv) is fulfilled. TS the number of time steps that was already mentioned. Training Record Section Next section Training Record, initiates the data fields of the tr variable. %% Training Record tr.best_epoch = 0; tr.goal = goal; tr.states =... {'epoch','time','perf','vperf','tperf','gradient','val_fail'}; tr.best_epoch indicates the number of an epoch in which the network gained best training results, before the training takes place it is the 0 epoch. The value of goal function goal (net.trainparam.goal) is assigned to the tr.goal field, and the tr.states field stores the statuses of network training. Status Section The Status section is used to open a window that shows the process of training (fig.1.) The window is generated by the nntraintool.m function,which in turn, isgenerated by the nn_train_feedback.m private function, that is started in the Status section. Generating of function is preceded with the initiation of the statusstructure, used for the window description. Fig.1. The window presents the training process of neural network 97
11 Train Section The last section of the traingd.m function is the Train section; it is the section where the training of neural network is realized.the section consists of a few blocks that are repeated iteratively. The condition of ending the iteration is gaining a demanded number of training epochs, saved in the net.trainparam.epochs field and meeting another criterion that is defined in the Stopping Criteria block. The first block of the section is the Gradient block. In this block only one function calcgx is generated, is used to compute the value of gx vector elements and the value of gradient. The gx vector, at a later stage, is used for the correction of weights and biases values, saved in the X vector % Gradient [gx,gradient] = calcgx(net,x,trainv.pd,zb,zi,zl,n,ac,el,perf,q,ts); The calcgx.m function required the following arguments: net the structure describing the studied neural network X current values of synaptic weights and biases, saved in the form of singular vector (created with the getx(net)) function trainv.pd the matrix of the delays of input signal samples in the network; Zb biases; Zi input weights; Zl weights of layers; N network inputs; Ac linked output layers; El errors of layers; perf value of goal function; Q number of training vectors for which the right training is created (trainv); TS number of time steps. The second block of the Train section is the StoppingCriteria block which was mentioned before. It groups all the conditions whose realization should stop the training process and leaving the iteration: % Stopping Criteria current_time = etime(clock,starttime); [userstop,usercancel] = nntraintool('check'); If userstop, tr.stop = 'User stop.'; net = best_net; 98
12 elseif usercancel, tr.stop = 'User cancel.'; net = original_net; elseif (perf<= goal), tr.stop = 'Performance goal met.'; net = best_net; elseif (epoch==epochs), tr.stop ='Maximum epoch reached.'; net = best_net; elseif (current_time>= time), tr.stop = 'Maximum time elapsed.'; net = best_net; elseif (gradient <= min_grad), tr.stop = 'Minimum gradient reached.'; net = best_net; elseif (dovalidation) && (val_fail>= max_fail), tr.stop = 'Validation stop.'; net = best_net; end After determining of the current time of network training by the etime function and saving it in the current_time variable, it starts to be checked whether a user has not pressed the StopTraining button or Cancel. The following block code is used to control whether any of conditions of stopping the training has been met; the following are checked in the following order: the userstop value signalling that the StopTraining button was pressed; the usercancel value signalling that the Cancel button was pressed; perf<= goal fulfilling of the condition means that the error, which was made by the network, is smaller than the maximum acceptable error the network has been trained; epoch == epochs meeting the condition means that a maximum acceptable number of training epochs was executed; current_time>= time meeting the condition means that the training time has exceeded the acceptable value; gradient <= min_grad meeting the condition means that the gradient is smaller than the acceptable, which means that the network is not actually being trained; (dovalidation) && (val_fail >= max_fail)whether the validation has been performer and the number of error training steps (causing thedeterioration of the goal function value) exceeded its acceptable amount. If any of the conditions is met, the comment that is appropriate for the situation is assigned to the tr.stop field. If the tr.stop fieldis not empty (some comment has been typed in it), the Stop block will causethe ending of the function process, and the accounts (saved in w tr.stop ) will show the user the reason of stopping the training [5]. 99
13 In another block Training record the fields of the tr variable are updated. An update of the fields is done by generating the tr_update.m script. Before the update a conditional instruction appears, that checks whether the logic value of the dotest variable is real. The dotest condition is real when the testing training set exist (that is the testv.indices variable contains at least one number of the testing index of training set). % Training record If dotest [tperf,ignore,testv.y] =... calcperf2(net,x,testv.pd,testv.tl,testv.ai,testv.q,testv.ts); end tr =... tr_update(tr,[epoch current_timeperfvperftperf gradient val_fail]); After the update of the fields of the tr variable, the update of parameters begins, which present: current number of training epochs, gradient value, value of goal function and time of training. These parameters are displayed in the nntraintool graphics window by generating the nn_train_feedback.m function with the 'update' argument. % Feedback nn_train_feedback('update',net,status,tr,{trainvvalvtestv},... [epoch,current_time,best_perf,gradient,val_fail]); The Stop block, in turn, with the use ofthe conditional instruction, checks whether the tr.stop field is not empty. If it contains any value, the operation of the for loop is ended with the break command. % Stop if ~isempty(tr.stop), break, end Another Gradient Descent block of the Train section is responsible for the update of weights and biases % Gradient Descent dx = lr*gx; X = X + dx; net = setx(net,x); [perf,el,trainv.y,ac,n,zb,zi,zl] = calcperf2(net,x,trainv.pd,trainv.tl,trainv.ai,q,ts); First, the correction value of weights vector is determined it is obtained by multiplying the value I gx (calculated in the calcgx.m function) by the training lr coefficient (net.trainparam.lr). In the next step, a new weights value is set by adding the selected dx correction to the current X weight value. The 100
14 setx(net,x)function does updates of the records of weights and biases in the net object. At the end of the block the calcperf2.m function calculates new error values, outputs, goal function. calcperf2.m errors, outputs, the objective function [6]. The last block of the section is the Validation block. In this block the values of validating set are calculated. It starts with a conditional instruction that checks if the logic variable dovalidation is real. The situation is analogue as in case of the conditional instruction which checks a logic value of the dotest variable. 4 Conclusions Matlab is a calculation-simulation environment that is commonly valued. Its great possibilities may be extended by creating own scripts and functions that use ready libraries. However, if one wants to use all functions of Matlab, they need to explore it thoroughly. In this article the analysis of the selected representative functions, that are used to train artificial neural networks, has been presented. Its analysis allows forwarding some general conclusions: the variable that describes the neural network (usually called net) is a structure but particular fields may have straight values or may be records; the variable that describes the neural network (net) contains all the information concerning the composition and training of neural network; some parameters are hidden; the training functions are divided into sections that are responsible for the realization of specific tasks; during the training process a lot of very technical help functions are generated; the parameters which are passed to the function, very often receive new names and new form in the function body. 5 Bibliography 1. Bartman J. Reguła PID uczenia sztucznych neuronów Metody Informatyki Stosowanej 3/2009 s. 5-19; 2. Beale M., Hagan M., Demuth H., - Neural Network Toolbox User's Guide - MathWorks , 3. MATLAB Programming Techniques - MathWors Werbos P. - The Roots of Backpropagation - New York, Willey Gomółka Z., Twaróg B., Bartman J.: Improvement of Image Processing by Using Homogeneus Neural Networks with Fractional Derivatives Theorem Dynamical Systems, Differential Equations and Applcations Vol 1 Suplement 2011 pp Gomółka Z., Twaróg B.: Artificial intelligence methods for image processing The Symbiosis of Engineering and Computer Science, Rzeszow 2010, ISBN , str
CONTROLO E DECISÃO INTELIGENTE 08/09
CONTROLO E DECISÃO INTELIGENTE 08/09 PL #5 MatLab Neural Networks Toolbox Alexandra Moutinho Example #1 Create a feedforward backpropagation network with a hidden layer. Here is a problem consisting of
More informationNeuro-Fuzzy Computing
CSE531 Neuro-Fuzzy Computing Tutorial/Assignment 2: Adaline and Multilayer Perceptron About this tutorial The objective of this tutorial is to study: You can create a single (composite) layer of neurons
More informationNeural Network Toolbox User's Guide
Neural Network Toolbox User's Guide Mark Hudson Beale Martin T. Hagan Howard B. Demuth R2015b How to Contact MathWorks Latest news: www.mathworks.com Sales and services: www.mathworks.com/sales_and_services
More informationNeural Network Toolbox User s Guide
Neural Network Toolbox User s Guide R2011b Mark Hudson Beale Martin T. Hagan Howard B. Demuth How to Contact MathWorks www.mathworks.com Web comp.soft-sys.matlab Newsgroup www.mathworks.com/contact_ts.html
More informationGdansk University of Technology Faculty of Electrical and Control Engineering Department of Control Systems Engineering
Gdansk University of Technology Faculty of Electrical and Control Engineering Department of Control Systems Engineering Artificial Intelligence Methods Neuron, neural layer, neural netorks - surface of
More informationA neural network that classifies glass either as window or non-window depending on the glass chemistry.
A neural network that classifies glass either as window or non-window depending on the glass chemistry. Djaber Maouche Department of Electrical Electronic Engineering Cukurova University Adana, Turkey
More information1 The Options and Structures in the Neural Net
1 The Options and Structures in the Neural Net These notes are broken into several parts to give you one place to look for the most salient features of a neural network. 1.1 Initialize the Neural Network
More informationNeural Networks. Lab 3: Multi layer perceptrons. Nonlinear regression and prediction.
Neural Networks. Lab 3: Multi layer perceptrons. Nonlinear regression and prediction. 1. Defining multi layer perceptrons. A multi layer perceptron (i.e. feedforward neural networks with hidden layers)
More informationCHAPTER VI BACK PROPAGATION ALGORITHM
6.1 Introduction CHAPTER VI BACK PROPAGATION ALGORITHM In the previous chapter, we analysed that multiple layer perceptrons are effectively applied to handle tricky problems if trained with a vastly accepted
More informationLinear Separability. Linear Separability. Capabilities of Threshold Neurons. Capabilities of Threshold Neurons. Capabilities of Threshold Neurons
Linear Separability Input space in the two-dimensional case (n = ): - - - - - - w =, w =, = - - - - - - w = -, w =, = - - - - - - w = -, w =, = Linear Separability So by varying the weights and the threshold,
More informationMATLAB representation of neural network Outline Neural network with single-layer of neurons. Neural network with multiple-layer of neurons.
MATLAB representation of neural network Outline Neural network with single-layer of neurons. Neural network with multiple-layer of neurons. Introduction: Neural Network topologies (Typical Architectures)
More informationCHAPTER 7 MASS LOSS PREDICTION USING ARTIFICIAL NEURAL NETWORK (ANN)
128 CHAPTER 7 MASS LOSS PREDICTION USING ARTIFICIAL NEURAL NETWORK (ANN) Various mathematical techniques like regression analysis and software tools have helped to develop a model using equation, which
More informationSupervised Learning in Neural Networks (Part 2)
Supervised Learning in Neural Networks (Part 2) Multilayer neural networks (back-propagation training algorithm) The input signals are propagated in a forward direction on a layer-bylayer basis. Learning
More information1. Approximation and Prediction Problems
Neural and Evolutionary Computing Lab 2: Neural Networks for Approximation and Prediction 1. Approximation and Prediction Problems Aim: extract from data a model which describes either the depence between
More informationAn Algorithm For Training Multilayer Perceptron (MLP) For Image Reconstruction Using Neural Network Without Overfitting.
An Algorithm For Training Multilayer Perceptron (MLP) For Image Reconstruction Using Neural Network Without Overfitting. Mohammad Mahmudul Alam Mia, Shovasis Kumar Biswas, Monalisa Chowdhury Urmi, Abubakar
More informationNeural Networks Laboratory EE 329 A
Neural Networks Laboratory EE 329 A Introduction: Artificial Neural Networks (ANN) are widely used to approximate complex systems that are difficult to model using conventional modeling techniques such
More informationPerformance Evaluation of Artificial Neural Networks for Spatial Data Analysis
Performance Evaluation of Artificial Neural Networks for Spatial Data Analysis Akram A. Moustafa 1*, Ziad A. Alqadi 2 and Eyad A. Shahroury 3 1 Department of Computer Science Al Al-Bayt University P.O.
More informationArtificial Neural Network Methodology for Modelling and Forecasting Maize Crop Yield
Agricultural Economics Research Review Vol. 21 January-June 2008 pp 5-10 Artificial Neural Network Methodology for Modelling and Forecasting Maize Crop Yield Rama Krishna Singh and Prajneshu * Biometrics
More informationPERFORMANCE COMPARISON OF BACK PROPAGATION AND RADIAL BASIS FUNCTION WITH MOVING AVERAGE FILTERING AND WAVELET DENOISING ON FETAL ECG EXTRACTION
I J C T A, 9(28) 2016, pp. 431-437 International Science Press PERFORMANCE COMPARISON OF BACK PROPAGATION AND RADIAL BASIS FUNCTION WITH MOVING AVERAGE FILTERING AND WAVELET DENOISING ON FETAL ECG EXTRACTION
More informationLECTURE NOTES Professor Anita Wasilewska NEURAL NETWORKS
LECTURE NOTES Professor Anita Wasilewska NEURAL NETWORKS Neural Networks Classifier Introduction INPUT: classification data, i.e. it contains an classification (class) attribute. WE also say that the class
More informationMultilayer Feed-forward networks
Multi Feed-forward networks 1. Computational models of McCulloch and Pitts proposed a binary threshold unit as a computational model for artificial neuron. This first type of neuron has been generalized
More informationAssignment # 5. Farrukh Jabeen Due Date: November 2, Neural Networks: Backpropation
Farrukh Jabeen Due Date: November 2, 2009. Neural Networks: Backpropation Assignment # 5 The "Backpropagation" method is one of the most popular methods of "learning" by a neural network. Read the class
More informationWeek 3: Perceptron and Multi-layer Perceptron
Week 3: Perceptron and Multi-layer Perceptron Phong Le, Willem Zuidema November 12, 2013 Last week we studied two famous biological neuron models, Fitzhugh-Nagumo model and Izhikevich model. This week,
More informationInternational Journal of Electrical and Computer Engineering 4: Application of Neural Network in User Authentication for Smart Home System
Application of Neural Network in User Authentication for Smart Home System A. Joseph, D.B.L. Bong, and D.A.A. Mat Abstract Security has been an important issue and concern in the smart home systems. Smart
More informationNEURAL NETWORK FOR PLC
NEURAL NETWORK FOR PLC L. Körösi, J. Paulusová Institute of Robotics and Cybernetics, Slovak University of Technology, Faculty of Electrical Engineering and Information Technology Abstract The aim of the
More informationCS6220: DATA MINING TECHNIQUES
CS6220: DATA MINING TECHNIQUES Image Data: Classification via Neural Networks Instructor: Yizhou Sun yzsun@ccs.neu.edu November 19, 2015 Methods to Learn Classification Clustering Frequent Pattern Mining
More informationPlanar Robot Arm Performance: Analysis with Feedforward Neural Networks
Planar Robot Arm Performance: Analysis with Feedforward Neural Networks Abraham Antonio López Villarreal, Samuel González-López, Luis Arturo Medina Muñoz Technological Institute of Nogales Sonora Mexico
More informationNeural Networks. CE-725: Statistical Pattern Recognition Sharif University of Technology Spring Soleymani
Neural Networks CE-725: Statistical Pattern Recognition Sharif University of Technology Spring 2013 Soleymani Outline Biological and artificial neural networks Feed-forward neural networks Single layer
More information6. NEURAL NETWORK BASED PATH PLANNING ALGORITHM 6.1 INTRODUCTION
6 NEURAL NETWORK BASED PATH PLANNING ALGORITHM 61 INTRODUCTION In previous chapters path planning algorithms such as trigonometry based path planning algorithm and direction based path planning algorithm
More informationImage Compression: An Artificial Neural Network Approach
Image Compression: An Artificial Neural Network Approach Anjana B 1, Mrs Shreeja R 2 1 Department of Computer Science and Engineering, Calicut University, Kuttippuram 2 Department of Computer Science and
More informationAn Intelligent Technique for Image Compression
An Intelligent Technique for Image Compression Athira Mayadevi Somanathan 1, V. Kalaichelvi 2 1 Dept. Of Electronics and Communications Engineering, BITS Pilani, Dubai, U.A.E. 2 Dept. Of Electronics and
More informationReification of Boolean Logic
Chapter Reification of Boolean Logic Exercises. (a) Design a feedforward network to divide the black dots from other corners with fewest neurons and layers. Please specify the values of weights and thresholds.
More informationClimate Precipitation Prediction by Neural Network
Journal of Mathematics and System Science 5 (205) 207-23 doi: 0.7265/259-529/205.05.005 D DAVID PUBLISHING Juliana Aparecida Anochi, Haroldo Fraga de Campos Velho 2. Applied Computing Graduate Program,
More informationArtificial Neural Networks Lecture Notes Part 5. Stephen Lucci, PhD. Part 5
Artificial Neural Networks Lecture Notes Part 5 About this file: If you have trouble reading the contents of this file, or in case of transcription errors, email gi0062@bcmail.brooklyn.cuny.edu Acknowledgments:
More informationNeural Network Neurons
Neural Networks Neural Network Neurons 1 Receives n inputs (plus a bias term) Multiplies each input by its weight Applies activation function to the sum of results Outputs result Activation Functions Given
More informationNotes on Multilayer, Feedforward Neural Networks
Notes on Multilayer, Feedforward Neural Networks CS425/528: Machine Learning Fall 2012 Prepared by: Lynne E. Parker [Material in these notes was gleaned from various sources, including E. Alpaydin s book
More informationNeuro-Fuzzy Inverse Forward Models
CS9 Autumn Neuro-Fuzzy Inverse Forward Models Brian Highfill Stanford University Department of Computer Science Abstract- Internal cognitive models are useful methods for the implementation of motor control
More informationALGORITHMS FOR INITIALIZATION OF NEURAL NETWORK WEIGHTS
ALGORITHMS FOR INITIALIZATION OF NEURAL NETWORK WEIGHTS A. Pavelka and A. Procházka Institute of Chemical Technology, Department of Computing and Control Engineering Abstract The paper is devoted to the
More informationPARALLEL TRAINING OF NEURAL NETWORKS FOR SPEECH RECOGNITION
PARALLEL TRAINING OF NEURAL NETWORKS FOR SPEECH RECOGNITION Stanislav Kontár Speech@FIT, Dept. of Computer Graphics and Multimedia, FIT, BUT, Brno, Czech Republic E-mail: xkonta00@stud.fit.vutbr.cz In
More informationA Neural Network Model Of Insurance Customer Ratings
A Neural Network Model Of Insurance Customer Ratings Jan Jantzen 1 Abstract Given a set of data on customers the engineering problem in this study is to model the data and classify customers
More informationEnsemble methods in machine learning. Example. Neural networks. Neural networks
Ensemble methods in machine learning Bootstrap aggregating (bagging) train an ensemble of models based on randomly resampled versions of the training set, then take a majority vote Example What if you
More informationNatural Language Processing CS 6320 Lecture 6 Neural Language Models. Instructor: Sanda Harabagiu
Natural Language Processing CS 6320 Lecture 6 Neural Language Models Instructor: Sanda Harabagiu In this lecture We shall cover: Deep Neural Models for Natural Language Processing Introduce Feed Forward
More information4.12 Generalization. In back-propagation learning, as many training examples as possible are typically used.
1 4.12 Generalization In back-propagation learning, as many training examples as possible are typically used. It is hoped that the network so designed generalizes well. A network generalizes well when
More information11/14/2010 Intelligent Systems and Soft Computing 1
Lecture 7 Artificial neural networks: Supervised learning Introduction, or how the brain works The neuron as a simple computing element The perceptron Multilayer neural networks Accelerated learning in
More informationThomas Nabelek September 22, ECE 7870 Project 1 Backpropagation
Thomas Nabelek ECE 7870 Project 1 Backpropagation 1) Introduction The backpropagation algorithm is a well-known method used to train an artificial neural network to sort inputs into their respective classes.
More informationDr. Qadri Hamarsheh Supervised Learning in Neural Networks (Part 1) learning algorithm Δwkj wkj Theoretically practically
Supervised Learning in Neural Networks (Part 1) A prescribed set of well-defined rules for the solution of a learning problem is called a learning algorithm. Variety of learning algorithms are existing,
More informationInternational Journal of Advanced Research in Computer Science and Software Engineering
Volume 3, Issue 4, April 203 ISSN: 77 2X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Stock Market Prediction
More informationSimulation of Zhang Suen Algorithm using Feed- Forward Neural Networks
Simulation of Zhang Suen Algorithm using Feed- Forward Neural Networks Ritika Luthra Research Scholar Chandigarh University Gulshan Goyal Associate Professor Chandigarh University ABSTRACT Image Skeletonization
More information7 Control Structures, Logical Statements
7 Control Structures, Logical Statements 7.1 Logical Statements 1. Logical (true or false) statements comparing scalars or matrices can be evaluated in MATLAB. Two matrices of the same size may be compared,
More informationDetecting central fixation by means of artificial neural networks in a pediatric vision screener using retinal birefringence scanning
BioMedical Engineering OnLine Ar ficial Neural Network for Detec ng Central Fixa on Hidden layer Output layer p 1 Input IW IW 1 *p Σ(iw 1,i p i ) + n 1 f 1 a 1 b 11 p 2 p 3 IW 2 *p Σ(iw 2,i p i ) b 12
More informationXES Tensorflow Process Prediction using the Tensorflow Deep-Learning Framework
XES Tensorflow Process Prediction using the Tensorflow Deep-Learning Framework Demo Paper Joerg Evermann 1, Jana-Rebecca Rehse 2,3, and Peter Fettke 2,3 1 Memorial University of Newfoundland 2 German Research
More informationThe AMORE Package. July 27, 2006
The AMORE Package July 27, 2006 Version 0.2-9 Date 2006-07-27 Title A MORE flexible neural network package Author Manuel CastejÃşn Limas, Joaquà n B. Ordieres MerÃl,Eliseo P. Vergara GonzÃąlez, Francisco
More informationNeural Networks (Overview) Prof. Richard Zanibbi
Neural Networks (Overview) Prof. Richard Zanibbi Inspired by Biology Introduction But as used in pattern recognition research, have little relation with real neural systems (studied in neurology and neuroscience)
More informationAPPLICATIONS OF INTELLIGENT HYBRID SYSTEMS IN MATLAB
APPLICATIONS OF INTELLIGENT HYBRID SYSTEMS IN MATLAB Z. Dideková, S. Kajan Institute of Control and Industrial Informatics, Faculty of Electrical Engineering and Information Technology, Slovak University
More informationNeural Network Classifier for Isolated Character Recognition
Neural Network Classifier for Isolated Character Recognition 1 Ruby Mehta, 2 Ravneet Kaur 1 M.Tech (CSE), Guru Nanak Dev University, Amritsar (Punjab), India 2 M.Tech Scholar, Computer Science & Engineering
More informationIn this assignment, we investigated the use of neural networks for supervised classification
Paul Couchman Fabien Imbault Ronan Tigreat Gorka Urchegui Tellechea Classification assignment (group 6) Image processing MSc Embedded Systems March 2003 Classification includes a broad range of decision-theoric
More informationAn Empirical Study of Software Metrics in Artificial Neural Networks
An Empirical Study of Software Metrics in Artificial Neural Networks WING KAI, LEUNG School of Computing Faculty of Computing, Information and English University of Central England Birmingham B42 2SU UNITED
More informationResearch on Evaluation Method of Product Style Semantics Based on Neural Network
Research Journal of Applied Sciences, Engineering and Technology 6(23): 4330-4335, 2013 ISSN: 2040-7459; e-issn: 2040-7467 Maxwell Scientific Organization, 2013 Submitted: September 28, 2012 Accepted:
More informationUnit V. Neural Fuzzy System
Unit V Neural Fuzzy System 1 Fuzzy Set In the classical set, its characteristic function assigns a value of either 1 or 0 to each individual in the universal set, There by discriminating between members
More informationClassification Lecture Notes cse352. Neural Networks. Professor Anita Wasilewska
Classification Lecture Notes cse352 Neural Networks Professor Anita Wasilewska Neural Networks Classification Introduction INPUT: classification data, i.e. it contains an classification (class) attribute
More informationAdaptive Regularization. in Neural Network Filters
Adaptive Regularization in Neural Network Filters Course 0455 Advanced Digital Signal Processing May 3 rd, 00 Fares El-Azm Michael Vinther d97058 s97397 Introduction The bulk of theoretical results and
More informationArtificial neural networks are the paradigm of connectionist systems (connectionism vs. symbolism)
Artificial Neural Networks Analogy to biological neural systems, the most robust learning systems we know. Attempt to: Understand natural biological systems through computational modeling. Model intelligent
More informationKINEMATIC ANALYSIS OF ADEPT VIPER USING NEURAL NETWORK
Proceedings of the National Conference on Trends and Advances in Mechanical Engineering, YMCA Institute of Engineering, Faridabad, Haryana., Dec 9-10, 2006. KINEMATIC ANALYSIS OF ADEPT VIPER USING NEURAL
More informationNeural Network Learning. Today s Lecture. Continuation of Neural Networks. Artificial Neural Networks. Lecture 24: Learning 3. Victor R.
Lecture 24: Learning 3 Victor R. Lesser CMPSCI 683 Fall 2010 Today s Lecture Continuation of Neural Networks Artificial Neural Networks Compose of nodes/units connected by links Each link has a numeric
More informationOptimization Methods for Machine Learning (OMML)
Optimization Methods for Machine Learning (OMML) 2nd lecture Prof. L. Palagi References: 1. Bishop Pattern Recognition and Machine Learning, Springer, 2006 (Chap 1) 2. V. Cherlassky, F. Mulier - Learning
More informationFor Monday. Read chapter 18, sections Homework:
For Monday Read chapter 18, sections 10-12 The material in section 8 and 9 is interesting, but we won t take time to cover it this semester Homework: Chapter 18, exercise 25 a-b Program 4 Model Neuron
More informationA Data Classification Algorithm of Internet of Things Based on Neural Network
A Data Classification Algorithm of Internet of Things Based on Neural Network https://doi.org/10.3991/ijoe.v13i09.7587 Zhenjun Li Hunan Radio and TV University, Hunan, China 278060389@qq.com Abstract To
More informationCHAPTER 6 COUNTER PROPAGATION NEURAL NETWORK IN GAIT RECOGNITION
75 CHAPTER 6 COUNTER PROPAGATION NEURAL NETWORK IN GAIT RECOGNITION 6.1 INTRODUCTION Counter propagation network (CPN) was developed by Robert Hecht-Nielsen as a means to combine an unsupervised Kohonen
More informationData Mining. Neural Networks
Data Mining Neural Networks Goals for this Unit Basic understanding of Neural Networks and how they work Ability to use Neural Networks to solve real problems Understand when neural networks may be most
More informationCOMP 551 Applied Machine Learning Lecture 14: Neural Networks
COMP 551 Applied Machine Learning Lecture 14: Neural Networks Instructor: (jpineau@cs.mcgill.ca) Class web page: www.cs.mcgill.ca/~jpineau/comp551 Unless otherwise noted, all material posted for this course
More informationThis leads to our algorithm which is outlined in Section III, along with a tabular summary of it's performance on several benchmarks. The last section
An Algorithm for Incremental Construction of Feedforward Networks of Threshold Units with Real Valued Inputs Dhananjay S. Phatak Electrical Engineering Department State University of New York, Binghamton,
More informationPractical Tips for using Backpropagation
Practical Tips for using Backpropagation Keith L. Downing August 31, 2017 1 Introduction In practice, backpropagation is as much an art as a science. The user typically needs to try many combinations of
More informationDeep Learning. Architecture Design for. Sargur N. Srihari
Architecture Design for Deep Learning Sargur N. srihari@cedar.buffalo.edu 1 Topics Overview 1. Example: Learning XOR 2. Gradient-Based Learning 3. Hidden Units 4. Architecture Design 5. Backpropagation
More informationDynamic Analysis of Structures Using Neural Networks
Dynamic Analysis of Structures Using Neural Networks Alireza Lavaei Academic member, Islamic Azad University, Boroujerd Branch, Iran Alireza Lohrasbi Academic member, Islamic Azad University, Boroujerd
More informationImproving the way neural networks learn Srikumar Ramalingam School of Computing University of Utah
Improving the way neural networks learn Srikumar Ramalingam School of Computing University of Utah Reference Most of the slides are taken from the third chapter of the online book by Michael Nielson: neuralnetworksanddeeplearning.com
More informationFast Learning for Big Data Using Dynamic Function
IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS Fast Learning for Big Data Using Dynamic Function To cite this article: T Alwajeeh et al 2017 IOP Conf. Ser.: Mater. Sci. Eng.
More informationSeismic regionalization based on an artificial neural network
Seismic regionalization based on an artificial neural network *Jaime García-Pérez 1) and René Riaño 2) 1), 2) Instituto de Ingeniería, UNAM, CU, Coyoacán, México D.F., 014510, Mexico 1) jgap@pumas.ii.unam.mx
More informationAPPLICATION OF A MULTI- LAYER PERCEPTRON FOR MASS VALUATION OF REAL ESTATES
FIG WORKING WEEK 2008 APPLICATION OF A MULTI- LAYER PERCEPTRON FOR MASS VALUATION OF REAL ESTATES Tomasz BUDZYŃSKI, PhD Artificial neural networks the highly sophisticated modelling technique, which allows
More information2. Neural network basics
2. Neural network basics Next commonalities among different neural networks are discussed in order to get started and show which structural parts or concepts appear in almost all networks. It is presented
More informationExercise: Training Simple MLP by Backpropagation. Using Netlab.
Exercise: Training Simple MLP by Backpropagation. Using Netlab. Petr Pošík December, 27 File list This document is an explanation text to the following script: demomlpklin.m script implementing the beckpropagation
More informationINVESTIGATING DATA MINING BY ARTIFICIAL NEURAL NETWORK: A CASE OF REAL ESTATE PROPERTY EVALUATION
http:// INVESTIGATING DATA MINING BY ARTIFICIAL NEURAL NETWORK: A CASE OF REAL ESTATE PROPERTY EVALUATION 1 Rajat Pradhan, 2 Satish Kumar 1,2 Dept. of Electronics & Communication Engineering, A.S.E.T.,
More informationSNIWD: Simultaneous Weight Noise Injection With Weight Decay for MLP Training
SNIWD: Simultaneous Weight Noise Injection With Weight Decay for MLP Training John Sum and Kevin Ho Institute of Technology Management, National Chung Hsing University Taichung 4, Taiwan. pfsum@nchu.edu.tw
More informationFeedback Alignment Algorithms. Lisa Zhang, Tingwu Wang, Mengye Ren
Feedback Alignment Algorithms Lisa Zhang, Tingwu Wang, Mengye Ren Agenda Review of Back Propagation Random feedback weights support learning in deep neural networks Direct Feedback Alignment Provides Learning
More informationIntroduction to MATLAB
Introduction to MATLAB Introduction MATLAB is an interactive package for numerical analysis, matrix computation, control system design, and linear system analysis and design available on most CAEN platforms
More informationPattern Classification Algorithms for Face Recognition
Chapter 7 Pattern Classification Algorithms for Face Recognition 7.1 Introduction The best pattern recognizers in most instances are human beings. Yet we do not completely understand how the brain recognize
More informationUsing the NNET Toolbox
CS 333 Neural Networks Spring Quarter 2002-2003 Dr. Asim Karim Basics of the Neural Networks Toolbox 4.0.1 MATLAB 6.1 includes in its collection of toolboxes a comprehensive API for developing neural networks.
More informationLearning. Learning agents Inductive learning. Neural Networks. Different Learning Scenarios Evaluation
Learning Learning agents Inductive learning Different Learning Scenarios Evaluation Slides based on Slides by Russell/Norvig, Ronald Williams, and Torsten Reil Material from Russell & Norvig, chapters
More informationCHAPTER 8 COMPOUND CHARACTER RECOGNITION USING VARIOUS MODELS
CHAPTER 8 COMPOUND CHARACTER RECOGNITION USING VARIOUS MODELS 8.1 Introduction The recognition systems developed so far were for simple characters comprising of consonants and vowels. But there is one
More informationMODELLING OF ARTIFICIAL NEURAL NETWORK CONTROLLER FOR ELECTRIC DRIVE WITH LINEAR TORQUE LOAD FUNCTION
MODELLING OF ARTIFICIAL NEURAL NETWORK CONTROLLER FOR ELECTRIC DRIVE WITH LINEAR TORQUE LOAD FUNCTION Janis Greivulis, Anatoly Levchenkov, Mikhail Gorobetz Riga Technical University, Faculty of Electrical
More informationInternational Research Journal of Computer Science (IRJCS) ISSN: Issue 09, Volume 4 (September 2017)
APPLICATION OF LRN AND BPNN USING TEMPORAL BACKPROPAGATION LEARNING FOR PREDICTION OF DISPLACEMENT Talvinder Singh, Munish Kumar C-DAC, Noida, India talvinder.grewaal@gmail.com,munishkumar@cdac.in Manuscript
More informationANNALS of the ORADEA UNIVERSITY. Fascicle of Management and Technological Engineering, Volume X (XX), 2011, NR2
MODELIG OF SURFACE ROUGHESS USIG MRA AD A METHOD Miroslav Radovanović 1, Miloš Madić University of iš, Faculty of Mechanical Engineering in iš, Serbia 1 mirado@masfak.ni.ac.rs, madic1981@gmail.com Keywords:
More informationTHE NEURAL NETWORKS: APPLICATION AND OPTIMIZATION APPLICATION OF LEVENBERG-MARQUARDT ALGORITHM FOR TIFINAGH CHARACTER RECOGNITION
International Journal of Science, Environment and Technology, Vol. 2, No 5, 2013, 779 786 ISSN 2278-3687 (O) THE NEURAL NETWORKS: APPLICATION AND OPTIMIZATION APPLICATION OF LEVENBERG-MARQUARDT ALGORITHM
More informationUse of Artificial Neural Networks to Investigate the Surface Roughness in CNC Milling Machine
Use of Artificial Neural Networks to Investigate the Surface Roughness in CNC Milling Machine M. Vijay Kumar Reddy 1 1 Department of Mechanical Engineering, Annamacharya Institute of Technology and Sciences,
More informationNeuron Selectivity as a Biologically Plausible Alternative to Backpropagation
Neuron Selectivity as a Biologically Plausible Alternative to Backpropagation C.J. Norsigian Department of Bioengineering cnorsigi@eng.ucsd.edu Vishwajith Ramesh Department of Bioengineering vramesh@eng.ucsd.edu
More informationDOUBLE-CURVED SURFACE FORMING PROCESS MODELING
7th International DAAAM Baltic Conference INDUSTRIAL ENGINEERING 22-24 April 2010, Tallinn, Estonia DOUBLE-CURVED SURFACE FORMING PROCESS MODELING Velsker, T.; Majak, J.; Eerme, M.; Pohlak, M. Abstract:
More informationApplication of a Back-Propagation Artificial Neural Network to Regional Grid-Based Geoid Model Generation Using GPS and Leveling Data
Application of a Back-Propagation Artificial Neural Network to Regional Grid-Based Geoid Model Generation Using GPS and Leveling Data Lao-Sheng Lin 1 Abstract: The height difference between the ellipsoidal
More informationAsst. Prof. Bhagwat Kakde
Volume 3, Issue 11, November 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Novel Approach
More informationIdentification of Multisensor Conversion Characteristic Using Neural Networks
Sensors & Transducers 3 by IFSA http://www.sensorsportal.com Identification of Multisensor Conversion Characteristic Using Neural Networks Iryna TURCHENKO and Volodymyr KOCHAN Research Institute of Intelligent
More informationCOMPUTATIONAL INTELLIGENCE
COMPUTATIONAL INTELLIGENCE Fundamentals Adrian Horzyk Preface Before we can proceed to discuss specific complex methods we have to introduce basic concepts, principles, and models of computational intelligence
More informationArtificial Neuron Modelling Based on Wave Shape
Artificial Neuron Modelling Based on Wave Shape Kieran Greer, Distributed Computing Systems, Belfast, UK. http://distributedcomputingsystems.co.uk Version 1.2 Abstract This paper describes a new model
More information