Data Mining For Multi-Criteria Energy Predictions
|
|
- Anne Shelton
- 6 years ago
- Views:
Transcription
1 Data Mnng For Mult-Crtera Energy Predctons Kashf Gll and Denns Moon Abstract We present a data mnng technque for mult-crtera predctons of wnd energy. A mult-crtera (MC) evolutonary computng method has been appled for the optmzaton of an Artfcal Intellgence learnng methodology Support Vector Machnes (SVM). The mult-crtera SVM method s appled and tested on a dataset wthn North Amerca, for predctons of wnd energy usng clmate varables. The SVM tranng employs Swarm Intellgence method for mult-crtera optmzaton. The Natonal Center for Envronmental Predcton (NCEP) s global reanalyss grdded dataset has been employed n ths study. The grdded dataset for ths partcular applcaton conssts of 4- ponts each consstng of fve varables. In order to study the mpact of hgher dmensons on the performance of SVM, Prncpal Component Analyss (PCA) s appled on the nput data to reduce the dmensonalty of the data. The results of mult-crtera SVM for the predcton of wnd energy are reported wth and wthout the pre-processng usng PCA. Index Terms Mult-Crtera optmzaton, Swarm Intellgence, Evolutonary computng, Prncpal Component Analyss, Support Vector Machnes, Wnd energy. I. INTRODUCTION The current paper descrbes a method for tranng support vector machne (SVM) n applcatons for wnd energy predctons at a wnd-farm level. The proposed methodology employs a Mult-Objectve Evolutonary Optmzaton approach for tranng the SVM. The SVM s a powerful learnng algorthm developed by Vapnk prmarly for classfcaton problems and later was extended to deal wth regresson [4 and 5]. The method s well-suted for the operatonal predctons and forecastng of wnd power, whch s an mportant varable for power utlty companes. The Mult-Objectve optmzaton approach dffers from sngle objectve n that the objectve to be optmzed s now a vector consstng of more than one objectve. The current mult-objectve methodology employs Swarm Intellgence based evolutonary computng mult-objectve strategy Manuscrpt receved August, 9. Kashf Gll and Denns Moon are wth WndLogcs, Inc., St Paul, MN 558 USA (correspondng author: ; fax: ; e-mal: kashf.gll@wndlogcs.com). called Mult-objectve Partcle Swarm Optmzaton (MOPSO) [6]. The PSO method has been developed for sngle objectve optmzaton by R. C. Eberhart and J. Kennedy []. It has been later extended to solve mult-objectve problems by varous researchers ncludng the method by [6]. The method orgnates from the swarm paradgm, called partcle swarm, and s expected to provde the so-called global or near-global optmum. PSO s characterzed by an adaptve algorthm based on a socal-psychologcal metaphor [] nvolvng ndvduals who are nteractng wth one another n a socal world. Ths sococogntve vew can be effectvely appled to computatonally ntellgent systems [8]. The governng factor n PSO s that the ndvduals, or partcles, keep track of ther best postons n the search space thus far obtaned, and also the best postons obtaned by ther neghborng partcles. The best poston of an ndvdual partcle s called local best, and the best of the postons obtaned by all the partcles s called the global best. Hence the global best s what all the partcles tend to follow. The algorthmc detals on PSO can be found n [,, 3, and 6]. The approach n [6] presents a multobjectve framework for SVM optmzaton usng MOPSO. The multobjectve approach to the PSO algorthm s mplemented by usng the concept of Pareto ranks and defnng the Pareto front on the objectve functon space. Mathematcally, a Pareto optmal front s defned as follows: A decson vector x S s called Pareto optmal f there does not exst another x S that domnates t. Let m P R be a set of vectors. The Pareto optmal front P * P contans all vectors x P, whch are not domnated by any vector x P : * P = x P / x P : x x () { } In the MOPSO algorthm, as devsed here [6], the partcles wll follow the nearest neghborng member of the Pareto front based on the proxmty n the objectve functon (soluton) space. At the same tme, the partcles n the front wll follow the best ndvdual n the front, whch s the medan (mddle partcle) of the Pareto front. The
2 term follow means assgnments done for each partcle n the populaton set to decde the drecton and offset (velocty) n the subsequent teraton. These assgnments are done based on the proxmty n the objectve functon or soluton space. The best ndvdual s defned n a relatve sense and may change from teraton to teraton dependng upon the value of objectve functon. The unque formulaton of MOPSO helps t to avod gettng struck n local optma, when makng a search n the mult-dmensonal parameter doman. In the current research, the MOPSO s used to parameterze the three parameters of SVM namely; the trade-off or cost parameter C, the epslon ε, and the kernel wdth γ. The MOPSO method uses a populaton of parameter sets to compete aganst each other through a number of teratons n order to mprove values of specfed mult-objectve crtera (objectve functons) e.g., root mean square error (RMSE), bas, hstogram error (BnRMSE), correlaton, etc. The optmum parameter search s conducted n an ntellgent manner by narrowng the desred regons of nterest and avods gettng struck n local optma. In our earler efforts, a sngle objectve optmzaton methodology has been employed for optmzaton of three SVM parameters. The approach was tested on a number of stes and results were encouragng. However, t has been notced that usng a sngle objectve optmzaton method can result n sub-optmal predctons when lookng at multple objectves. The sngle objectve formulaton (usng PSO) employed coeffcent of determnaton (COD ), as the only objectve functon, but t was notced that the resultng dstrbutons were hghly dstorted when compared to the observed dstrbutons. The coeffcent of determnaton (COD) s lnearly related to RMSE and can range between nf to ; the value of beng a perfect ft. COD n = = n = ( O P ) ( O O ) where O stands for observed, P stands for predcted, O stands for mean of observed, and s the ndex that goes from to the length of tme seres n. It s shown n Fgure where a trade-off curve s presented between BnRMSE vs. COD. It can be notced that COD value ncreases wth the ncrease n BnRMSE value. The correspondng hstograms are also shown n Fgure for each of the extreme ends (maxmum COD and mnmum BnRMSE) and the best compromse soluton from the curve. The hstograms shown n Fgure make t clear that the best COD (or RMSE) s the one wth hghest BnRMSE and ndeed msses the extreme ends of the dstrbuton. Thus no matter how temptng t s to acheve best COD value t does not cover the extreme ends of the dstrbuton. On the other hand the best BnRMSE comes at the cost of lowest COD (or hghest RMSE) and s not desred ether. Thus t s requred to have a mult-objectve scheme that smultaneously mnmze these objectves and provde a trade-off surface and therefore a compromse soluton can be chosen between the two objectves. Fgure also shows the hstogram for the best compromse soluton whch provdes a decent dstrbuton when compared to observed data. O D C BnRMS vs COD Best BnRMS Best COD Compromse BnRMS Fgure : Trade-off curve between BnRMSE and COD II. MATERIAL AND METHOD The current procedures prmarly employ SVM for buldng regresson models for assessng and forecastng wnd resources. The prmary nputs to the SVM come from the Natonal Center for Envronmental Predcton (NCEP) s reanalyss grdded data [7] centered on the wnd farm locaton. The target s the measurements of wnd farm aggregate power. The current tranng uses a k-fold cross valdaton scheme referred to as Round-Robn strategy. The dea wthn the Round-Robn s to dvde the avalable tranng data nto two sets; use one for tranng and hold the other for testng the model. In ths partcular Round-Robn strategy data s dvded nto months. The tranng s done on all the months except one, and the testng s done on the hold-out month. The current operatonal methods employ manual calbraton for the SVM parameters n assessment projects and a smple grd-based parameter search n forecastng applcatons. The goal n usng MOPSO s to explore the regons of nterest wth respect to the specfc mult-objectve crtera n an effcent way. Another attractve feature of MOPSO s that t results n a so-called Pareto parameter space, whch accounts for parameter uncertanty between the two objectve functons. Thus the result s an ensemble of parameter sets cluttered around the so called global optmum w.r.t. the mult-objectve space. Ths ensemble of parameter sets also gves tradeoffs on dfferent objectve crtera. The MOPSO-SVM method s tested on the data from an operatonal assessment ste n North Amerca. The results are compared wth observed data usng a number of evaluaton crtera on the valdaton sets. As stated above, the data from 4 NCEP s grd ponts each consstng of 5 varables (total varables) s used.
3 5 Best BnRMS Compromse Best COD 5 ts C oun Power (kw) Fgure : Hstogram comparson between, BnRMS best, COD best, and the best compromse soluton x 4 Fgure 3: Trade-off curve between the two objectves BnRMS vs. RMSE for SVM and PCA-SVM The problem therefore has a dmensonalty of and can pose a dffcult task for SVM. In order to study ths mpact, Prncpal Component Analyss (PCA) s appled and the PCs explanng 95% of the varance are ncluded as nputs to SVM. The comparson s made wth SVM that does not use PCA as pre-processng step. MOPSO requre a populaton consstng of parameter sets to be evolved through a number of teratons competng aganst each other to obtan an optmum (mnmum n ths case) value for the BnRMSE and RMSE. In the current formulaton, a 5 member populaton s evolved for teratons wthn MOPSO for wnd power predctons at the wnd farm. III. RESULTS AND DISCUSSION The MOPSO-SVM results are shown wth and wthout PCA pre-processng. Ths wnd farm ste s located n Canada and has 9 months of energy data avalable. The MOPSO s used to tran SVM over the avalable 7 months of tranng data (@ 6-hourly tme resoluton) usng a Round-Robn cross-valdaton strategy. Ths gves an opportunty to tran on 6 months and test the results on a hold-out one month test set. By repeatng the process for all the 7 months, gves a full 7 months of test data to compare aganst the observed. Snce there s 9 months of data avalable for ths ste, a full one- year of data s used n valdaton (completely unseen data). The results that follow are the predctons on the valdaton set. The results are shown on the normalzed (between - and ) dataset.
4 .3.. d te c P red SVM PCA-SVM Fgure 4: Scatter plot for the mean monthly wnd energy data for SVM and PCA-SVM PCA SVM SVM Power (kw) Fgure 5: Hstogram for SVM and PCA-SVM along wth observed The trade-off curve for MOPSO SVM optmzaton for the two objectves s shown n Fgure 3. The trade-off between BnRMS vs. RMSE s shown for the SVM usng orgnal nput compared aganst SVM usng Prncpal Components (PCs) as nputs. A best ft lne has also been shown for the two curves. It can be notced that there s lttle dfference between the two approaches. The PCA-SVM produced a better objectve functon result for BnRMS, where as smple SVM provded a better objectve functon result for RMSE. Fgure 4 shows the monthly mean wnd power for the months compared aganst the observed data. The results are shown for SVM predcton wth and wthout the pre-processng usng PCA. As stated above, there s a very lttle dfference between the two approaches and a good ft has been found. It can be notced that predctons are n reasonable agreement wth the observed data. The results n Fgure 5 show hstogram of observed vs. the predcted wnd power data at the 6-hourly tme resoluton (the predcton tme step). The results are shown for SVM predcton wth and wthout the pre-processng usng PCA. It can be notced that the dstrbutons are well-mantaned usng MOPSO methodology and a reasonable agreement between observed and predcted power s evdent from the fgure. A number of goodness-of-ft measures are evaluated n Table, whch are monthly root mean square error (RMSE), monthly coeffcent of determnaton (COD), nstantaneous RMSE, nstantaneous COD, and BnRMSE (hstogram bn RMSE). The results n Table are presented for SVM predcton wth and wthout the pre-processng usng PCA. Both monthly and nstantaneous wnd power are of sgnfcant nterest, and thus are ncluded n the current analyss. It can be notced that not only monthly but also nstantaneous power predctons are n close agreement wth the observed.
5 Table : Wnd energy goodness-of-ft MC-SVM Goodness measure SVM PCA-SVM Monthly RMSE Monthly COD Instantaneous RMSE Instantaneous COD BnRMSE IV. CONCLUSIONS In the present paper, a mult-objectve evolutonary computng method MOPSO s used to optmze the three parameters of SVM for wnd energy predctons. The approach has been tested on data from a wnd farm usng NCEP s re-analyss grd data. The predcton strategy employs SVM wth and wthout the pre-processng usng PCA. A number of graphcal and tabular results n the form of goodness-of-ft measures are presented for wnd energy predctons. The SVM predctons at the wnd farm level produced excellent agreement wth the observed data for the valdaton set. The results for the two approaches are qute smlar but SVM wthout any pre-processng usng PCA produced slghtly better results. Overall, the results have been encouragng and t s recommended to use MOPSO-SVM approach for other operatonal projects n the area of wnd power predctons and forecastng. Whle further modfcatons and advancements are underway, the current procedure s sound enough to be appled n operatonal settngs. REFERENCES [] Eberhart, R. C., and J. Kennedy, A new optmzer usng partcle swarm theory, n Proceedngs of the Sxth Internatonal Symposum on Mcro Machne and Human Scence, 995, MHS 95, 995, pp , do:.9/mhs , IEEE Press, Pscataway, N. J. [] Kennedy, J., and R. C. Eberhart, Partcle swarm optmzaton, n Proceedngs of IEEE Internatonal Conference on Neural Networks, IV, vol. 4, 995, pp , do:.9/icnn , IEEE Press, Pscataway, N. J. [3] Eberhart, R. C., R. W. Dobbns, and P. Smpson, Computatonal Intellgence PC Tools, Elsever, New York, 996. [4] Vapnk, V., The Nature of Statstcal Learnng Theory, Sprnger, New York, 995. [5] Vapnk, V., Statstcal Learnng Theory, John Wley, Hoboken, N. J., 998. [6] Gll, M. K., Y. H. Kahel, A. Khall, M. McKee, and L. Bastdas, Multobjectve partcle swarm optmzaton for parameter estmaton n hydrology, Water Resour. Res., 4, 6, W747, do:.9/5wr458. [7] Kalnay et al.,the NCEP/NCAR 4-year reanalyss project, Bull. Amer. Meteor. Soc., 77, 996, [8] Kennedy, J., R. C. Eberhart, and Y. Sh, (). Swarm Intellgence.Morgan Kaufmann, San Francsco, CA.
Support Vector Machines
/9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.
More informationSmoothing Spline ANOVA for variable screening
Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory
More informationSimulation: Solving Dynamic Models ABE 5646 Week 11 Chapter 2, Spring 2010
Smulaton: Solvng Dynamc Models ABE 5646 Week Chapter 2, Sprng 200 Week Descrpton Readng Materal Mar 5- Mar 9 Evaluatng [Crop] Models Comparng a model wth data - Graphcal, errors - Measures of agreement
More informationSum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints
Australan Journal of Basc and Appled Scences, 2(4): 1204-1208, 2008 ISSN 1991-8178 Sum of Lnear and Fractonal Multobjectve Programmng Problem under Fuzzy Rules Constrants 1 2 Sanjay Jan and Kalash Lachhwan
More informationParallelism for Nested Loops with Non-uniform and Flow Dependences
Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr
More informationCHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION
24 CHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION The present chapter proposes an IPSO approach for multprocessor task schedulng problem wth two classfcatons, namely, statc ndependent tasks and
More informationSupport Vector Machines
Support Vector Machnes Decson surface s a hyperplane (lne n 2D) n feature space (smlar to the Perceptron) Arguably, the most mportant recent dscovery n machne learnng In a nutshell: map the data to a predetermned
More informationClassifier Swarms for Human Detection in Infrared Imagery
Classfer Swarms for Human Detecton n Infrared Imagery Yur Owechko, Swarup Medasan, and Narayan Srnvasa HRL Laboratores, LLC 3011 Malbu Canyon Road, Malbu, CA 90265 {owechko, smedasan, nsrnvasa}@hrl.com
More informationCluster Analysis of Electrical Behavior
Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School
More informationProblem Definitions and Evaluation Criteria for Computational Expensive Optimization
Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty
More informationOptimizing SVR using Local Best PSO for Software Effort Estimation
Journal of Informaton Technology and Computer Scence Volume 1, Number 1, 2016, pp. 28 37 Journal Homepage: www.jtecs.ub.ac.d Optmzng SVR usng Local Best PSO for Software Effort Estmaton Dnda Novtasar 1,
More informationA mathematical programming approach to the analysis, design and scheduling of offshore oilfields
17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 A mathematcal programmng approach to the analyss, desgn and
More informationLearning the Kernel Parameters in Kernel Minimum Distance Classifier
Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department
More information12/2/2009. Announcements. Parametric / Non-parametric. Case-Based Reasoning. Nearest-Neighbor on Images. Nearest-Neighbor Classification
Introducton to Artfcal Intellgence V22.0472-001 Fall 2009 Lecture 24: Nearest-Neghbors & Support Vector Machnes Rob Fergus Dept of Computer Scence, Courant Insttute, NYU Sldes from Danel Yeung, John DeNero
More informationClassifying Acoustic Transient Signals Using Artificial Intelligence
Classfyng Acoustc Transent Sgnals Usng Artfcal Intellgence Steve Sutton, Unversty of North Carolna At Wlmngton (suttons@charter.net) Greg Huff, Unversty of North Carolna At Wlmngton (jgh7476@uncwl.edu)
More informationComplexity Analysis of Problem-Dimension Using PSO
Proceedngs of the 7th WSEAS Internatonal Conference on Evolutonary Computng, Cavtat, Croata, June -4, 6 (pp45-5) Complexty Analyss of Problem-Dmenson Usng PSO BUTHAINAH S. AL-KAZEMI AND SAMI J. HABIB,
More informationClassification / Regression Support Vector Machines
Classfcaton / Regresson Support Vector Machnes Jeff Howbert Introducton to Machne Learnng Wnter 04 Topcs SVM classfers for lnearly separable classes SVM classfers for non-lnearly separable classes SVM
More informationImproving Classifier Fusion Using Particle Swarm Optimization
Proceedngs of the 7 IEEE Symposum on Computatonal Intellgence n Multcrtera Decson Makng (MCDM 7) Improvng Classfer Fuson Usng Partcle Swarm Optmzaton Kalyan Veeramachanen Dept. of EECS Syracuse Unversty
More informationS1 Note. Basis functions.
S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type
More informationA Fast Content-Based Multimedia Retrieval Technique Using Compressed Data
A Fast Content-Based Multmeda Retreval Technque Usng Compressed Data Borko Furht and Pornvt Saksobhavvat NSF Multmeda Laboratory Florda Atlantc Unversty, Boca Raton, Florda 3343 ABSTRACT In ths paper,
More informationSLAM Summer School 2006 Practical 2: SLAM using Monocular Vision
SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,
More informationClassifier Selection Based on Data Complexity Measures *
Classfer Selecton Based on Data Complexty Measures * Edth Hernández-Reyes, J.A. Carrasco-Ochoa, and J.Fco. Martínez-Trndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.
More informationDetermining the Optimal Bandwidth Based on Multi-criterion Fusion
Proceedngs of 01 4th Internatonal Conference on Machne Learnng and Computng IPCSIT vol. 5 (01) (01) IACSIT Press, Sngapore Determnng the Optmal Bandwdth Based on Mult-crteron Fuson Ha-L Lang 1+, Xan-Mn
More informationFEATURE EXTRACTION. Dr. K.Vijayarekha. Associate Dean School of Electrical and Electronics Engineering SASTRA University, Thanjavur
FEATURE EXTRACTION Dr. K.Vjayarekha Assocate Dean School of Electrcal and Electroncs Engneerng SASTRA Unversty, Thanjavur613 41 Jont Intatve of IITs and IISc Funded by MHRD Page 1 of 8 Table of Contents
More informationEdge Detection in Noisy Images Using the Support Vector Machines
Edge Detecton n Nosy Images Usng the Support Vector Machnes Hlaro Gómez-Moreno, Saturnno Maldonado-Bascón, Francsco López-Ferreras Sgnal Theory and Communcatons Department. Unversty of Alcalá Crta. Madrd-Barcelona
More informationCourse Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms
Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques
More informationMULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION
MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION Paulo Quntlano 1 & Antono Santa-Rosa 1 Federal Polce Department, Brasla, Brazl. E-mals: quntlano.pqs@dpf.gov.br and
More informationSkew Angle Estimation and Correction of Hand Written, Textual and Large areas of Non-Textual Document Images: A Novel Approach
Angle Estmaton and Correcton of Hand Wrtten, Textual and Large areas of Non-Textual Document Images: A Novel Approach D.R.Ramesh Babu Pyush M Kumat Mahesh D Dhannawat PES Insttute of Technology Research
More informationAPPLICATION OF A COMPUTATIONALLY EFFICIENT GEOSTATISTICAL APPROACH TO CHARACTERIZING VARIABLY SPACED WATER-TABLE DATA
RFr"W/FZD JAN 2 4 1995 OST control # 1385 John J Q U ~ M Argonne Natonal Laboratory Argonne, L 60439 Tel: 708-252-5357, Fax: 708-252-3 611 APPLCATON OF A COMPUTATONALLY EFFCENT GEOSTATSTCAL APPROACH TO
More informationFeature Reduction and Selection
Feature Reducton and Selecton Dr. Shuang LIANG School of Software Engneerng TongJ Unversty Fall, 2012 Today s Topcs Introducton Problems of Dmensonalty Feature Reducton Statstc methods Prncpal Components
More informationAir Transport Demand. Ta-Hui Yang Associate Professor Department of Logistics Management National Kaohsiung First Univ. of Sci. & Tech.
Ar Transport Demand Ta-Hu Yang Assocate Professor Department of Logstcs Management Natonal Kaohsung Frst Unv. of Sc. & Tech. 1 Ar Transport Demand Demand for ar transport between two ctes or two regons
More informationRecommended Items Rating Prediction based on RBF Neural Network Optimized by PSO Algorithm
Recommended Items Ratng Predcton based on RBF Neural Network Optmzed by PSO Algorthm Chengfang Tan, Cayn Wang, Yuln L and Xx Q Abstract In order to mtgate the data sparsty and cold-start problems of recommendaton
More informationOptimal Design of Nonlinear Fuzzy Model by Means of Independent Fuzzy Scatter Partition
Optmal Desgn of onlnear Fuzzy Model by Means of Independent Fuzzy Scatter Partton Keon-Jun Park, Hyung-Kl Kang and Yong-Kab Km *, Department of Informaton and Communcaton Engneerng, Wonkwang Unversty,
More informationOutline. Discriminative classifiers for image recognition. Where in the World? A nearest neighbor recognition example 4/14/2011. CS 376 Lecture 22 1
4/14/011 Outlne Dscrmnatve classfers for mage recognton Wednesday, Aprl 13 Krsten Grauman UT-Austn Last tme: wndow-based generc obect detecton basc ppelne face detecton wth boostng as case study Today:
More informationMachine Learning 9. week
Machne Learnng 9. week Mappng Concept Radal Bass Functons (RBF) RBF Networks 1 Mappng It s probably the best scenaro for the classfcaton of two dataset s to separate them lnearly. As you see n the below
More informationAn Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation
17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 An Iteratve Soluton Approach to Process Plant Layout usng Mxed
More informationUsing Neural Networks and Support Vector Machines in Data Mining
Usng eural etworks and Support Vector Machnes n Data Mnng RICHARD A. WASIOWSKI Computer Scence Department Calforna State Unversty Domnguez Hlls Carson, CA 90747 USA Abstract: - Multvarate data analyss
More informationA Notable Swarm Approach to Evolve Neural Network for Classification in Data Mining
A Notable Swarm Approach to Evolve Neural Network for Classfcaton n Data Mnng Satchdananda Dehur 1, Bjan Bhar Mshra 2 and Sung-Bae Cho 1 1 Soft Computng Laboratory, Department of Computer Scence, Yonse
More informationProgramming in Fortran 90 : 2017/2018
Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values
More informationNUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS
ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana
More informationTN348: Openlab Module - Colocalization
TN348: Openlab Module - Colocalzaton Topc The Colocalzaton module provdes the faclty to vsualze and quantfy colocalzaton between pars of mages. The Colocalzaton wndow contans a prevew of the two mages
More informationFace Recognition University at Buffalo CSE666 Lecture Slides Resources:
Face Recognton Unversty at Buffalo CSE666 Lecture Sldes Resources: http://www.face-rec.org/algorthms/ Overvew of face recognton algorthms Correlaton - Pxel based correspondence between two face mages Structural
More informationSVM-based Learning for Multiple Model Estimation
SVM-based Learnng for Multple Model Estmaton Vladmr Cherkassky and Yunqan Ma Department of Electrcal and Computer Engneerng Unversty of Mnnesota Mnneapols, MN 55455 {cherkass,myq}@ece.umn.edu Abstract:
More informationA MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS
Proceedngs of the Wnter Smulaton Conference M E Kuhl, N M Steger, F B Armstrong, and J A Jones, eds A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Mark W Brantley Chun-Hung
More informationGSLM Operations Research II Fall 13/14
GSLM 58 Operatons Research II Fall /4 6. Separable Programmng Consder a general NLP mn f(x) s.t. g j (x) b j j =. m. Defnton 6.. The NLP s a separable program f ts objectve functon and all constrants are
More informationOutline. Self-Organizing Maps (SOM) US Hebbian Learning, Cntd. The learning rule is Hebbian like:
Self-Organzng Maps (SOM) Turgay İBRİKÇİ, PhD. Outlne Introducton Structures of SOM SOM Archtecture Neghborhoods SOM Algorthm Examples Summary 1 2 Unsupervsed Hebban Learnng US Hebban Learnng, Cntd 3 A
More informationA generalized multiobjective particle swarm optimization solver for spreadsheet models: application to water quality
Hydrology Days 2006 A generalzed multobjectve partcle swarm optmzaton solver for spreadsheet models: applcaton to water qualty Alexandre M. Baltar 1 Water Resources Plannng and Management Dvson, Dept.
More informationCS246: Mining Massive Datasets Jure Leskovec, Stanford University
CS46: Mnng Massve Datasets Jure Leskovec, Stanford Unversty http://cs46.stanford.edu /19/013 Jure Leskovec, Stanford CS46: Mnng Massve Datasets, http://cs46.stanford.edu Perceptron: y = sgn( x Ho to fnd
More informationNetwork Intrusion Detection Based on PSO-SVM
TELKOMNIKA Indonesan Journal of Electrcal Engneerng Vol.1, No., February 014, pp. 150 ~ 1508 DOI: http://dx.do.org/10.11591/telkomnka.v1.386 150 Network Intruson Detecton Based on PSO-SVM Changsheng Xang*
More informationX- Chart Using ANOM Approach
ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are
More informationClustering Algorithm Combining CPSO with K-Means Chunqin Gu 1, a, Qian Tao 2, b
Internatonal Conference on Advances n Mechancal Engneerng and Industral Informatcs (AMEII 05) Clusterng Algorthm Combnng CPSO wth K-Means Chunqn Gu, a, Qan Tao, b Department of Informaton Scence, Zhongka
More informationMeta-heuristics for Multidimensional Knapsack Problems
2012 4th Internatonal Conference on Computer Research and Development IPCSIT vol.39 (2012) (2012) IACSIT Press, Sngapore Meta-heurstcs for Multdmensonal Knapsack Problems Zhbao Man + Computer Scence Department,
More informationA Fast Visual Tracking Algorithm Based on Circle Pixels Matching
A Fast Vsual Trackng Algorthm Based on Crcle Pxels Matchng Zhqang Hou hou_zhq@sohu.com Chongzhao Han czhan@mal.xjtu.edu.cn Ln Zheng Abstract: A fast vsual trackng algorthm based on crcle pxels matchng
More informationAssignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.
Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton
More informationEmpirical Distributions of Parameter Estimates. in Binary Logistic Regression Using Bootstrap
Int. Journal of Math. Analyss, Vol. 8, 4, no. 5, 7-7 HIKARI Ltd, www.m-hkar.com http://dx.do.org/.988/jma.4.494 Emprcal Dstrbutons of Parameter Estmates n Bnary Logstc Regresson Usng Bootstrap Anwar Ftranto*
More informationLECTURE NOTES Duality Theory, Sensitivity Analysis, and Parametric Programming
CEE 60 Davd Rosenberg p. LECTURE NOTES Dualty Theory, Senstvty Analyss, and Parametrc Programmng Learnng Objectves. Revew the prmal LP model formulaton 2. Formulate the Dual Problem of an LP problem (TUES)
More informationOptimizing Document Scoring for Query Retrieval
Optmzng Document Scorng for Query Retreval Brent Ellwen baellwe@cs.stanford.edu Abstract The goal of ths project was to automate the process of tunng a document query engne. Specfcally, I used machne learnng
More informationData Mining: Model Evaluation
Data Mnng: Model Evaluaton Aprl 16, 2013 1 Issues: Evaluatng Classfcaton Methods Accurac classfer accurac: predctng class label predctor accurac: guessng value of predcted attrbutes Speed tme to construct
More informationAn Improved Particle Swarm Optimization for Feature Selection
Journal of Bonc Engneerng 8 (20)?????? An Improved Partcle Swarm Optmzaton for Feature Selecton Yuannng Lu,2, Gang Wang,2, Hulng Chen,2, Hao Dong,2, Xaodong Zhu,2, Sujng Wang,2 Abstract. College of Computer
More informationRelated-Mode Attacks on CTR Encryption Mode
Internatonal Journal of Network Securty, Vol.4, No.3, PP.282 287, May 2007 282 Related-Mode Attacks on CTR Encrypton Mode Dayn Wang, Dongda Ln, and Wenlng Wu (Correspondng author: Dayn Wang) Key Laboratory
More informationA Binarization Algorithm specialized on Document Images and Photos
A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a
More informationA Modified Median Filter for the Removal of Impulse Noise Based on the Support Vector Machines
A Modfed Medan Flter for the Removal of Impulse Nose Based on the Support Vector Machnes H. GOMEZ-MORENO, S. MALDONADO-BASCON, F. LOPEZ-FERRERAS, M. UTRILLA- MANSO AND P. GIL-JIMENEZ Departamento de Teoría
More informationAdaptive Virtual Support Vector Machine for the Reliability Analysis of High-Dimensional Problems
Proceedngs of the ASME 2 Internatonal Desgn Engneerng Techncal Conferences & Computers and Informaton n Engneerng Conference IDETC/CIE 2 August 29-3, 2, Washngton, D.C., USA DETC2-47538 Adaptve Vrtual
More informationBOOSTING CLASSIFICATION ACCURACY WITH SAMPLES CHOSEN FROM A VALIDATION SET
1 BOOSTING CLASSIFICATION ACCURACY WITH SAMPLES CHOSEN FROM A VALIDATION SET TZU-CHENG CHUANG School of Electrcal and Computer Engneerng, Purdue Unversty, West Lafayette, Indana 47907 SAUL B. GELFAND School
More informationContent Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers
IOSR Journal of Electroncs and Communcaton Engneerng (IOSR-JECE) e-issn: 78-834,p- ISSN: 78-8735.Volume 9, Issue, Ver. IV (Mar - Apr. 04), PP 0-07 Content Based Image Retreval Usng -D Dscrete Wavelet wth
More informationMachine Learning. Support Vector Machines. (contains material adapted from talks by Constantin F. Aliferis & Ioannis Tsamardinos, and Martin Law)
Machne Learnng Support Vector Machnes (contans materal adapted from talks by Constantn F. Alfers & Ioanns Tsamardnos, and Martn Law) Bryan Pardo, Machne Learnng: EECS 349 Fall 2014 Support Vector Machnes
More informationRelevance Assignment and Fusion of Multiple Learning Methods Applied to Remote Sensing Image Analysis
Assgnment and Fuson of Multple Learnng Methods Appled to Remote Sensng Image Analyss Peter Bajcsy, We-Wen Feng and Praveen Kumar Natonal Center for Supercomputng Applcaton (NCSA), Unversty of Illnos at
More informationWishing you all a Total Quality New Year!
Total Qualty Management and Sx Sgma Post Graduate Program 214-15 Sesson 4 Vnay Kumar Kalakband Assstant Professor Operatons & Systems Area 1 Wshng you all a Total Qualty New Year! Hope you acheve Sx sgma
More informationPRÉSENTATIONS DE PROJETS
PRÉSENTATIONS DE PROJETS Rex Onlne (V. Atanasu) What s Rex? Rex s an onlne browser for collectons of wrtten documents [1]. Asde ths core functon t has however many other applcatons that make t nterestng
More informationOutline. Type of Machine Learning. Examples of Application. Unsupervised Learning
Outlne Artfcal Intellgence and ts applcatons Lecture 8 Unsupervsed Learnng Professor Danel Yeung danyeung@eee.org Dr. Patrck Chan patrckchan@eee.org South Chna Unversty of Technology, Chna Introducton
More informationMultiobjective fuzzy optimization method
Buletnul Ştnţfc al nverstăţ "Poltehnca" dn Tmşoara Sera ELECTRONICĂ ş TELECOMNICAŢII TRANSACTIONS on ELECTRONICS and COMMNICATIONS Tom 49(63, Fasccola, 24 Multobjectve fuzzy optmzaton method Gabrel Oltean
More informationMulti-objective Optimization Using Adaptive Explicit Non-Dominated Region Sampling
11 th World Congress on Structural and Multdscplnary Optmsaton 07 th -12 th, June 2015, Sydney Australa Mult-objectve Optmzaton Usng Adaptve Explct Non-Domnated Regon Samplng Anrban Basudhar Lvermore Software
More informationResearch of Neural Network Classifier Based on FCM and PSO for Breast Cancer Classification
Research of Neural Network Classfer Based on FCM and PSO for Breast Cancer Classfcaton Le Zhang 1, Ln Wang 1, Xujewen Wang 2, Keke Lu 2, and Ajth Abraham 3 1 Shandong Provncal Key Laboratory of Network
More informationVectorization of Image Outlines Using Rational Spline and Genetic Algorithm
01 Internatonal Conference on Image, Vson and Computng (ICIVC 01) IPCSIT vol. 50 (01) (01) IACSIT Press, Sngapore DOI: 10.776/IPCSIT.01.V50.4 Vectorzaton of Image Outlnes Usng Ratonal Splne and Genetc
More informationNGPM -- A NSGA-II Program in Matlab
Verson 1.4 LIN Song Aerospace Structural Dynamcs Research Laboratory College of Astronautcs, Northwestern Polytechncal Unversty, Chna Emal: lsssswc@163.com 2011-07-26 Contents Contents... 1. Introducton...
More informationMULTIOBJECTIVE OPTIMIZATION USING PARALLEL VECTOR EVALUATED PARTICLE SWARM OPTIMIZATION
MULTIOBJECTIVE OPTIMIZATION USING PARALLEL VECTOR EVALUATED PARTICLE OPTIMIZATION K.E. Parsopoulos, D.K. Tasouls, M.N. Vrahats Department of Mathematcs, Unversty of Patras Artfcal Intellgence Research
More informationThe Research of Support Vector Machine in Agricultural Data Classification
The Research of Support Vector Machne n Agrcultural Data Classfcaton Le Sh, Qguo Duan, Xnmng Ma, Me Weng College of Informaton and Management Scence, HeNan Agrcultural Unversty, Zhengzhou 45000 Chna Zhengzhou
More informationHierarchical clustering for gene expression data analysis
Herarchcal clusterng for gene expresson data analyss Gorgo Valentn e-mal: valentn@ds.unm.t Clusterng of Mcroarray Data. Clusterng of gene expresson profles (rows) => dscovery of co-regulated and functonally
More informationBiostatistics 615/815
The E-M Algorthm Bostatstcs 615/815 Lecture 17 Last Lecture: The Smplex Method General method for optmzaton Makes few assumptons about functon Crawls towards mnmum Some recommendatons Multple startng ponts
More informationParallel matrix-vector multiplication
Appendx A Parallel matrx-vector multplcaton The reduced transton matrx of the three-dmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more
More informationSubspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;
Subspace clusterng Clusterng Fundamental to all clusterng technques s the choce of dstance measure between data ponts; D q ( ) ( ) 2 x x = x x, j k = 1 k jk Squared Eucldean dstance Assumpton: All features
More informationThe Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique
//00 :0 AM Outlne and Readng The Greedy Method The Greedy Method Technque (secton.) Fractonal Knapsack Problem (secton..) Task Schedulng (secton..) Mnmum Spannng Trees (secton.) Change Money Problem Greedy
More informationComparison of Heuristics for Scheduling Independent Tasks on Heterogeneous Distributed Environments
Comparson of Heurstcs for Schedulng Independent Tasks on Heterogeneous Dstrbuted Envronments Hesam Izakan¹, Ath Abraham², Senor Member, IEEE, Václav Snášel³ ¹ Islamc Azad Unversty, Ramsar Branch, Ramsar,
More informationA Statistical Model Selection Strategy Applied to Neural Networks
A Statstcal Model Selecton Strategy Appled to Neural Networks Joaquín Pzarro Elsa Guerrero Pedro L. Galndo joaqun.pzarro@uca.es elsa.guerrero@uca.es pedro.galndo@uca.es Dpto Lenguajes y Sstemas Informátcos
More informationMaximum Variance Combined with Adaptive Genetic Algorithm for Infrared Image Segmentation
Internatonal Conference on Logstcs Engneerng, Management and Computer Scence (LEMCS 5) Maxmum Varance Combned wth Adaptve Genetc Algorthm for Infrared Image Segmentaton Huxuan Fu College of Automaton Harbn
More informationAn Entropy-Based Approach to Integrated Information Needs Assessment
Dstrbuton Statement A: Approved for publc release; dstrbuton s unlmted. An Entropy-Based Approach to ntegrated nformaton Needs Assessment June 8, 2004 Wllam J. Farrell Lockheed Martn Advanced Technology
More informationNAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics
Introducton G10 NAG Fortran Lbrary Chapter Introducton G10 Smoothng n Statstcs Contents 1 Scope of the Chapter... 2 2 Background to the Problems... 2 2.1 Smoothng Methods... 2 2.2 Smoothng Splnes and Regresson
More informationTerm Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task
Proceedngs of NTCIR-6 Workshop Meetng, May 15-18, 2007, Tokyo, Japan Term Weghtng Classfcaton System Usng the Ch-square Statstc for the Classfcaton Subtask at NTCIR-6 Patent Retreval Task Kotaro Hashmoto
More informationProblem Definitions and Evaluation Criteria for the CEC 2015 Competition on Learning-based Real-Parameter Single Objective Optimization
Problem Defntons and Evaluaton Crtera for the CEC 15 Competton on Learnng-based Real-Parameter Sngle Objectve Optmzaton J. J. Lang 1, B. Y. Qu, P. N. Suganthan 3, Q. Chen 4 1 School of Electrcal Engneerng,
More informationTuning of Fuzzy Inference Systems Through Unconstrained Optimization Techniques
Tunng of Fuzzy Inference Systems Through Unconstraned Optmzaton Technques ROGERIO ANDRADE FLAUZINO, IVAN NUNES DA SILVA Department of Electrcal Engneerng State Unversty of São Paulo UNESP CP 473, CEP 733-36,
More informationRange images. Range image registration. Examples of sampling patterns. Range images and range surfaces
Range mages For many structured lght scanners, the range data forms a hghly regular pattern known as a range mage. he samplng pattern s determned by the specfc scanner. Range mage regstraton 1 Examples
More informationThe Codesign Challenge
ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.
More informationParameters Optimization of SVM Based on Improved FOA and Its Application in Fault Diagnosis
Parameters Optmzaton of SVM Based on Improved FOA and Its Applcaton n Fault Dagnoss Qantu Zhang1*, Lqng Fang1, Sca Su, Yan Lv1 1 Frst Department, Mechancal Engneerng College, Shjazhuang, Hebe Provnce,
More informationTraining ANFIS Structure with Modified PSO Algorithm
Proceedngs of the 5th Medterranean Conference on Control & Automaton, July 7-9, 007, Athens - Greece T4-003 Tranng ANFIS Structure wth Modfed PSO Algorthm V.Seyd Ghomsheh *, M. Alyar Shoorehdel **, M.
More informationIMAGE FUSION TECHNIQUES
Int. J. Chem. Sc.: 14(S3), 2016, 812-816 ISSN 0972-768X www.sadgurupublcatons.com IMAGE FUSION TECHNIQUES A Short Note P. SUBRAMANIAN *, M. SOWNDARIYA, S. SWATHI and SAINTA MONICA ECE Department, Aarupada
More informationLecture 5: Multilayer Perceptrons
Lecture 5: Multlayer Perceptrons Roger Grosse 1 Introducton So far, we ve only talked about lnear models: lnear regresson and lnear bnary classfers. We noted that there are functons that can t be represented
More informationArtificial Intelligence (AI) methods are concerned with. Artificial Intelligence Techniques for Steam Generator Modelling
Artfcal Intellgence Technques for Steam Generator Modellng Sarah Wrght and Tshldz Marwala Abstract Ths paper nvestgates the use of dfferent Artfcal Intellgence methods to predct the values of several contnuous
More information3. CR parameters and Multi-Objective Fitness Function
3 CR parameters and Mult-objectve Ftness Functon 41 3. CR parameters and Mult-Objectve Ftness Functon 3.1. Introducton Cogntve rados dynamcally confgure the wreless communcaton system, whch takes beneft
More informationHelsinki University Of Technology, Systems Analysis Laboratory Mat Independent research projects in applied mathematics (3 cr)
Helsnk Unversty Of Technology, Systems Analyss Laboratory Mat-2.08 Independent research projects n appled mathematcs (3 cr) "! #$&% Antt Laukkanen 506 R ajlaukka@cc.hut.f 2 Introducton...3 2 Multattrbute
More informationImprovement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration
Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,
More information