Optimal Design of Nonlinear Fuzzy Model by Means of Independent Fuzzy Scatter Partition
|
|
- Suzan Russell
- 6 years ago
- Views:
Transcription
1 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, 344-2, Shnyong-dong, Iksan-s, Chonbuk, South Korea Abstract. We ntroduce a optmal desgn of fuzzy model by means of ndependent fuzzy scatter partton to contruct the nonlnear model. The fuzzy rules of fuzzy model are generated by parttonng the nput space n the fuzzy scatter form. The premse parameters of the rules are determned by membersh matr by means of FCM clusterng algorthm that has ndependent fuzzfcaton factors. The consequence part of the rules s represented n the form of polynomal functons. And the optmal process s conducted by PSO to desgn the optmal model. The proposed model s evaluated usng the data wdely used n nonlnear process. Keywords: Fuzzy Scatter Partton, Fuzzy Model, Fuzzy C-Means Clusterng Algorthm, Independent Fuzzfcaton Factor, Partcle Swarm Optmzaton. 1 Introducton Fuzzy sets have been wdely nvestgated and the fuzzy model s a popular computng framework based on the concepts of fuzzy sets, fuzzy f-then rules, and fuzzy reasonng [1]. It has found successful applcatons n a wde varety of felds. Lngustc modelng [2] and fuzzy relaton equaton-based approach [3] were proposed as prmordal dentfcaton methods for fuzzy models. The general class of Sugeno-Takag models [4] gave rse to more sophstcated rule-based systems. In fuzzy modelng, the structure and parameter dentfcaton are usually concerned [5],[6]. The desgners fnd t dffcult to develop adequate fuzzy rules and membersh functons to reflect the essence of the data. The generaton of fuzzy rules has the problem that the number of fuzzy rules eponentally ncreases. In ths paper, we ntroduce a fuzzy model based on ndependent fuzzy scatter partton of nput space. Independent fuzzy partton realzed wth fuzzy c-means (FCM) clusterng [7] help determne the fuzzy rules of fuzzy model. The premse part of the rules s realzed wth the ad of the scatter partton of nput space generated by FCM clusterng algorthms. The number of the partton of nput space equals the number of clusters and the ndvdual parttoned spaces descrbe the rules. The consequence part of the rules s represented by polynomal functons. We also * Correspondng author : ykm@wonkwang.ac.kr AICT 2013, ASTL Vol. 26, pp , 2013 SERSC
2 Proceedngs, The 1st Internatonal Conference on Advanced Informaton and Computer Technology optmze the parameters of the fuzzy model usng partcle swarm optmzaton (PSO) algorthm [8]. The proposed model s evaluated wth numercal epermentaton to deal wth the nonlnear process. 2 Independent Fuzzy Scatter Partton-based Fuzzy Model 2.1 Premse Identfcaton The premse part of the FIS s developed by means of the fuzzy c-means clusterng algorthm [7]. Ths algorthm dvdes the nput space by the clusters and each parttoned local space represents the fuzzy rules. Therefore, the number of clusters s equal to the number of rules. Ths algorthm s amed at the formaton of c clusters (relatons) n R n. Consder the set X, whch conssts of data ponts treated as vectors located n some n-dmensonal Eucldean space, that s X={ 1, 2,, }, p R n. In clusterng we assgn patterns p X nto c clusters, whch are represented by ts prototypes v R n. The assgnment to ndvdual clusters s epressed n terms of the partton matr U = [u ] where c u 1 0 u p 1 1, 1 p, 1 c (1). (2) The obectve functon Q gudng the clusterng s epressed as a sum of the dstances of ndvdual data from the prototypes v 1, v 2,, and v c, Q c 1 p 1 u m p v 2. (3) Here denotes the Eucldean dstance; m stands for a fuzzfcaton coeffcent, m >1.0. The resultng partton matr s denoted by U = [u ]. The mnmzaton of Q s realzed through successve teratons by adustng both the prototypes and entres of the partton matr, that s mn Q(U, v 1, v 2,, v c ). The correspondng formulas used n an teratve fashon read as follows. m m v u p u (4) p 1 p 1 u 1 c 1 p p v v 2 m 1. (5) 184
3 Optmal Desgn of onlnear Fuzzy Model by Means of Independent Fuzzy Scatter Partton The resultng partton matr obtaned from (5) becomes the frng strengths of fuzzy rules. The dentfcaton of the concluson parts of the rules deals wth a selecton of ther structure that s followed by the determnaton of the respectve parameters of the local functons occurrng there. The concluson s epressed as follows. R : If and 1 d 1 d and s F Then y f (,, ). (6) Type 1 (Smplfed Inference): f a 0 Type 2 (Lnear Inference): f a 0 d k 1 a k k Where R s the -th rule, k represents the nput varables, F s a membersh grades (matr) obtaned by usng FCM clusterng algorthm, a s are coeffcent of polynomal functon. The calculatons of the numerc output of the model, based on the actvaton (matchng) levels of the rules there, are carred out n the well-known format y * n 1 w p y n 1 w p n 1 wˆ y. p (7) 3 Optmzaton of the Proposed Model Partcle swarm optmzaton (PSO) [8] was proposed by Kennedy, Eberhart to smulate socal behavor by representng the movement of a brd flock or fsh school. PSO s a computatonal algorthm that optmzes a gven problem by teratvely tryng to mprove canddate solutons (partcles). PSO algorthm optmzes a gven problem by havng a swarm of partcles and movng these partcles around n the search space. Each partcle's movement s affected by ts local best postons and s also guded toward the global best postons n the search-space over the partcle's poston and velocty. And these postons are updated as better postons. The swarm move toward the best solutons. In order to optmze the parameters of the proposed model, we determned the fuzzfcaton factors of the clusters composed of the premse part of the fuzzy rules. 4 Epermental Studes We dscuss numercal eample n order to evaluate the advantages and the effectveness of the proposed approach. The tme seres data (296 nput-output pars) comng from the gas furnace nonlnear process has been ntensvely studed n the prevous lterature [9]. The delayed terms of methane gas flow rate u(t) and carbon dode densty y(t) are used as nput varables organzed n a vector format as [u(t-3), 186
4 Proceedngs, The 1st Internatonal Conference on Advanced Informaton and Computer Technology u(t-2), u(t-1), y(t-3), y(t-2), y(t-1)]. y(t) s the output varable. The frst part of the data set (consstng of 148 pars) was used for tranng purposes. The remanng part of the seres serves as a testng data set. We consder the MSE as a performance nde. We construct the model for a two-dmensonal system by confgurng 2-nput 1- output system usng u(t-3) and y(t-1) as nputs. And we epermented wth the model usng the parameters outlned n Table 1. Table 1. Intal parameter of PSO Parameters Value Generaton 150 Swarm sze 50 V ma 20% Inerta weght [ ] Acceleraton constants 2.0 We epermented by optmzng the parameters of the fuzzfcaton factors of the clusters n two methods usng PSO. One tunng method s a method that all clusters have the same fuzzfcaton factor and another tunng method s a method that each cluster has the ndependent fuzzfcaton factor. Table 2 summarzes the performance nde for tranng and testng data by settng the number of clusters and nference type. Here, PI and E_PI stand for the performance nde for the tranng data set and the testng data set, respectvely. Ths table shows that the proposed approach has better results. From Table 2, we selected the best model wth fve rules (clusters) wth lnear nference that ehbts PI = and E_PI = Table 2. Performance of the proposed model (a) Smplfed Inference Same fuzzfcaton factor Independent fuzzfcaton factors o. of Clusters PI E_PI PI E_PI (b) Lnear Inference Same fuzzfcaton factor Independent fuzzfcaton factors o. of Clusters PI E_PI PI E_PI
5 Optmal Desgn of onlnear Fuzzy Model by Means of Independent Fuzzy Scatter Partton Fgure 1 shows ndependent fuzzy-parttoned nput spaces usng FCM clusterng algorthm for the selected model. Fgure 2(a) depct that the selected model has three clusters because the other two clusters n the selected model ddn t have the patterns. (a) Local spaces (b) Membersh matr Fg. 1. Independent parttoned nput spaces usng FCM clusterng algorthm. Fgure 2 presents the optmzaton procedure for fve partcles and performance nde for the selected model usng PSO. The model outputs of tranng data and testng data for the selected model are presented n fgure 3. (a) Partcles Fg. 2. Optmzaton process. (b) Performance nde (a) Tranng data Fg. 3. Orgnal and model outputs. (b) Testng data 188
6 Proceedngs, The 1st Internatonal Conference on Advanced Informaton and Computer Technology 5 Conclusons In ths paper, we ntroduced a fuzzy model based on ndependent fuzzy scatter partton of nput space. The nput spaces of the proposed model were dvded as the scatter form usng FCM clusterng algorthm to generate the rules of the system for nonlnear process. And the proposed fuzzy model was optmzed by PSO to fnd the best values of the fuzzfcaton factors to reflect the characterstcs of the data. By ths method, we could allevate the problem of the curse of dmensonalty and desgn the fuzzy model that s compact and smple. From the results n the prevous secton, we were able to desgn preferred model wth a very small number of rules that has the appromaton abltes and the generalzaton capabltes. Acknowledgements. Ths research was supported by Basc Scence Research Program through the atonal Research Foundaton of Korea (RF) funded by the Mnstry of Educaton (2013R1A1A ). References 1. Jang, J.S.R, Mzutan, E., Sun, C.T.: euro-fuzzy and Soft Computng, A Computatonal Approach to Learnng and Machne Intellgence. Prentce Hall, J, (1997) 2. Tong, R.M.: Synthess of fuzzy models for ndustral processes. Int. J Gen Syst. 4, (1978) 3. Pedrycz, W.: umercal and applcaton aspects of fuzzy relatonal equatons. Fuzzy Sets Syst. 11, (1983) 4. Takag, T., Sugeno, M.: Fuzzy dentfcaton of systems and ts applcatons to modelng and control. IEEE Trans Syst, Cybern. SMC-15(1), (1985) 5. Mana, Y., and Benreeb, M.: ew Condton of Stablsaton for Contnuous Takag-Sugeno Fuzzy System based on Fuzzy Lyapunov Functon, Internatonal Journal of Control and Automaton, 4, 3, , (2011) 6. Park, K. J., Lee, D. Y. and Lee, J. P., Desgn of FCM-based fuzzy neural networks and ts optmzaton for pattern recognton. Communcatons n Computer an Informaton Scence, 261 CCIS, , (2011) 7. Bezdek, J. C.: Pattern Recognton wth Fuzzy Obectve Functon Algorthms, Plenum Press, ew York, (1981) 8. Kennedy, J., Eberhart, R., Partcle Swarm Optmzaton. Proceedngs of IEEE Internatonal Conference on eural etworks, 4, , (1995) 9. Bo, G.E.P., Jenkns, G.M.: Tme Seres Analyss: Forecastng and Control, 2nd ed., Holden-Day, San Francsco, CA (1976) 188
Sum 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 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 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 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 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 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 informationAn Application of Fuzzy c-means Clustering to FLC Design for Electric Ceramics Kiln
An Applcaton of cmeans Clusterng to FLC Desgn for lectrc Ceramcs Kln Watcharacha Wryasuttwong, Somphop Rodamporn lectrcal ngneerng Department, Faculty of ngneerng, Srnaharnwrot Unversty, Nahornnayo 6,
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 informationStudy on Fuzzy Models of Wind Turbine Power Curve
Proceedngs of the 006 IASME/WSEAS Internatonal Conference on Energy & Envronmental Systems, Chalkda, Greece, May 8-0, 006 (pp-7) Study on Fuzzy Models of Wnd Turbne Power Curve SHU-CHEN WANG PEI-HWA HUANG
More informationMachine Learning: Algorithms and Applications
14/05/1 Machne Learnng: Algorthms and Applcatons Florano Zn Free Unversty of Bozen-Bolzano Faculty of Computer Scence Academc Year 011-01 Lecture 10: 14 May 01 Unsupervsed Learnng cont Sldes courtesy of
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 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 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 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 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 informationFuzzy Model Identification Using Support Vector Clustering Method
Fuzzy Model Identfcaton Usng Support Vector Clusterng Method $\úhj OUçar, Yakup Demr, and Cüneyt * ]HOLú Electrcal and Electroncs Engneerng Department, Engneerng Faculty, ÕUDW Unversty, Elazg, Turkey agulucar@eee.org,ydemr@frat.edu.tr
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 informationType-2 Fuzzy Non-uniform Rational B-spline Model with Type-2 Fuzzy Data
Malaysan Journal of Mathematcal Scences 11(S) Aprl : 35 46 (2017) Specal Issue: The 2nd Internatonal Conference and Workshop on Mathematcal Analyss (ICWOMA 2016) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES
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 informationInvestigations of Topology and Shape of Multi-material Optimum Design of Structures
Advanced Scence and Tecnology Letters Vol.141 (GST 2016), pp.241-245 ttp://dx.do.org/10.14257/astl.2016.141.52 Investgatons of Topology and Sape of Mult-materal Optmum Desgn of Structures Quoc Hoan Doan
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 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 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 informationSupport 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 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 informationIncremental Learning with Support Vector Machines and Fuzzy Set Theory
The 25th Workshop on Combnatoral Mathematcs and Computaton Theory Incremental Learnng wth Support Vector Machnes and Fuzzy Set Theory Yu-Mng Chuang 1 and Cha-Hwa Ln 2* 1 Department of Computer Scence and
More informationData Mining For Multi-Criteria Energy Predictions
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
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 informationReview of approximation techniques
CHAPTER 2 Revew of appromaton technques 2. Introducton Optmzaton problems n engneerng desgn are characterzed by the followng assocated features: the objectve functon and constrants are mplct functons evaluated
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 informationPerformance Evaluation of an ANFIS Based Power System Stabilizer Applied in Multi-Machine Power Systems
Performance Evaluaton of an ANFIS Based Power System Stablzer Appled n Mult-Machne Power Systems A. A GHARAVEISI 1,2 A.DARABI 3 M. MONADI 4 A. KHAJEH-ZADEH 5 M. RASHIDI-NEJAD 1,2,5 1. Shahd Bahonar Unversty
More informationAn Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices
Internatonal Mathematcal Forum, Vol 7, 2012, no 52, 2549-2554 An Applcaton of the Dulmage-Mendelsohn Decomposton to Sparse Null Space Bases of Full Row Rank Matrces Mostafa Khorramzadeh Department of Mathematcal
More informationThe Comparison of Calibration Method of Binocular Stereo Vision System Ke Zhang a *, Zhao Gao b
3rd Internatonal Conference on Materal, Mechancal and Manufacturng Engneerng (IC3ME 2015) The Comparson of Calbraton Method of Bnocular Stereo Vson System Ke Zhang a *, Zhao Gao b College of Engneerng,
More informationA new paradigm of fuzzy control point in space curve
MATEMATIKA, 2016, Volume 32, Number 2, 153 159 c Penerbt UTM Press All rghts reserved A new paradgm of fuzzy control pont n space curve 1 Abd Fatah Wahab, 2 Mohd Sallehuddn Husan and 3 Mohammad Izat Emr
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 informationThe Shortest Path of Touring Lines given in the Plane
Send Orders for Reprnts to reprnts@benthamscence.ae 262 The Open Cybernetcs & Systemcs Journal, 2015, 9, 262-267 The Shortest Path of Tourng Lnes gven n the Plane Open Access Ljuan Wang 1,2, Dandan He
More informationFace Recognition Based on SVM and 2DPCA
Vol. 4, o. 3, September, 2011 Face Recognton Based on SVM and 2DPCA Tha Hoang Le, Len Bu Faculty of Informaton Technology, HCMC Unversty of Scence Faculty of Informaton Scences and Engneerng, Unversty
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 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 informationA New Approach For the Ranking of Fuzzy Sets With Different Heights
New pproach For the ankng of Fuzzy Sets Wth Dfferent Heghts Pushpnder Sngh School of Mathematcs Computer pplcatons Thapar Unversty, Patala-7 00 Inda pushpndersnl@gmalcom STCT ankng of fuzzy sets plays
More informationAngle-Independent 3D Reconstruction. Ji Zhang Mireille Boutin Daniel Aliaga
Angle-Independent 3D Reconstructon J Zhang Mrelle Boutn Danel Alaga Goal: Structure from Moton To reconstruct the 3D geometry of a scene from a set of pctures (e.g. a move of the scene pont reconstructon
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 informationUSING MODIFIED FUZZY PARTICLE SWARM OPTIMIZATION ALGORITHM FOR PARAMETER ESTIMATION OF SURGE ARRESTERS MODELS
Internatonal Journal of Innovatve Computng, Informaton and Control ICIC Internatonal c 2012 ISSN 1349-4198 Volume 8, Number 1(B), January 2012 pp. 567 581 USING MODIFIED FUZZY PARTICLE SWARM OPTIMIZATION
More informationAn Accurate Evaluation of Integrals in Convex and Non convex Polygonal Domain by Twelve Node Quadrilateral Finite Element Method
Internatonal Journal of Computatonal and Appled Mathematcs. ISSN 89-4966 Volume, Number (07), pp. 33-4 Research Inda Publcatons http://www.rpublcaton.com An Accurate Evaluaton of Integrals n Convex and
More informationHigh-Boost Mesh Filtering for 3-D Shape Enhancement
Hgh-Boost Mesh Flterng for 3-D Shape Enhancement Hrokazu Yagou Λ Alexander Belyaev y Damng We z Λ y z ; ; Shape Modelng Laboratory, Unversty of Azu, Azu-Wakamatsu 965-8580 Japan y Computer Graphcs Group,
More informationCollaboratively Regularized Nearest Points for Set Based Recognition
Academc Center for Computng and Meda Studes, Kyoto Unversty Collaboratvely Regularzed Nearest Ponts for Set Based Recognton Yang Wu, Mchhko Mnoh, Masayuk Mukunok Kyoto Unversty 9/1/013 BMVC 013 @ Brstol,
More informationAn Improved Image Segmentation Algorithm Based on the Otsu Method
3th ACIS Internatonal Conference on Software Engneerng, Artfcal Intellgence, Networkng arallel/dstrbuted Computng An Improved Image Segmentaton Algorthm Based on the Otsu Method Mengxng Huang, enjao Yu,
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 informationCorrelative features for the classification of textural images
Correlatve features for the classfcaton of textural mages M A Turkova 1 and A V Gadel 1, 1 Samara Natonal Research Unversty, Moskovskoe Shosse 34, Samara, Russa, 443086 Image Processng Systems Insttute
More informationModeling of a Class of Nonlinear Dynamic System
Sensors & ransducers, Vol. 69, Issue 4, Aprl 4, pp. 53-58 Sensors & ransducers 4 by IFSA Publshng, S. L. http://www.sensorsportal.com Modelng of a Class of Nonlnear Dynamc System Guangjun LIU, Xaopng XU,
More informationAn Optimal Algorithm for Prufer Codes *
J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,
More informationGA-Based Learning Algorithms to Identify Fuzzy Rules for Fuzzy Neural Networks
Seventh Internatonal Conference on Intellgent Systems Desgn and Applcatons GA-Based Learnng Algorthms to Identfy Fuzzy Rules for Fuzzy Neural Networks K Almejall, K Dahal, Member IEEE, and A Hossan, Member
More informationLoad-Balanced Anycast Routing
Load-Balanced Anycast Routng Chng-Yu Ln, Jung-Hua Lo, and Sy-Yen Kuo Department of Electrcal Engneerng atonal Tawan Unversty, Tape, Tawan sykuo@cc.ee.ntu.edu.tw Abstract For fault-tolerance and load-balance
More informationFinite Element Analysis of Rubber Sealing Ring Resilience Behavior Qu Jia 1,a, Chen Geng 1,b and Yang Yuwei 2,c
Advanced Materals Research Onlne: 03-06-3 ISSN: 66-8985, Vol. 705, pp 40-44 do:0.408/www.scentfc.net/amr.705.40 03 Trans Tech Publcatons, Swtzerland Fnte Element Analyss of Rubber Sealng Rng Reslence Behavor
More informationBIN XIA et al: AN IMPROVED K-MEANS ALGORITHM BASED ON CLOUD PLATFORM FOR DATA MINING
An Improved K-means Algorthm based on Cloud Platform for Data Mnng Bn Xa *, Yan Lu 2. School of nformaton and management scence, Henan Agrcultural Unversty, Zhengzhou, Henan 450002, P.R. Chna 2. College
More informationSolving two-person zero-sum game by Matlab
Appled Mechancs and Materals Onlne: 2011-02-02 ISSN: 1662-7482, Vols. 50-51, pp 262-265 do:10.4028/www.scentfc.net/amm.50-51.262 2011 Trans Tech Publcatons, Swtzerland Solvng two-person zero-sum game by
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 informationOptimum Synthesis of Mechanisms For Path Generation Using a New Curvature Based Deflection Based Objective Function
Proceedngs of the 6th WSEAS Internatonal Conference on Smulaton, Modellng and Optmzaton, Lsbon, Portugal, September -4, 6 67 Optmum Synthess of Mechansms For Path Generaton Usng a ew Curvature Based Deflecton
More informationControl strategies for network efficiency and resilience with route choice
Control strateges for networ effcency and reslence wth route choce Andy Chow Ru Sha Centre for Transport Studes Unversty College London, UK Centralsed strateges UK 1 Centralsed strateges Some effectve
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 informationA fast algorithm for color image segmentation
Unersty of Wollongong Research Onlne Faculty of Informatcs - Papers (Arche) Faculty of Engneerng and Informaton Scences 006 A fast algorthm for color mage segmentaton L. Dong Unersty of Wollongong, lju@uow.edu.au
More informationUsing Particle Swarm Optimization for Enhancing the Hierarchical Cell Relay Routing Protocol
2012 Thrd Internatonal Conference on Networkng and Computng Usng Partcle Swarm Optmzaton for Enhancng the Herarchcal Cell Relay Routng Protocol Hung-Y Ch Department of Electrcal Engneerng Natonal Sun Yat-Sen
More informationUsing an Adaptive Neuro-Fuzzy Inference System (AnFis) Algorithm for Automatic Diagnosis of Skin Cancer
Journal of Communcaton and Computer 8 (2011) 751-755 Usng an Adaptve Neuro-Fuzzy Inference System (AnFs) Algorthm for Automatc Dagnoss of Skn Cancer Suhal M. Odeh Department of Computer Informaton Systems,
More informationA soft computing approach for modeling of severity of faults in software systems
Internatonal Journal of Physcal Scences Vol. 5 (), pp. 074-085, February, 010 Avalable onlne at http://www.academcjournals.org/ijps ISSN 199-1950 010 Academc Journals Full Length Research Paper A soft
More informationChinese Word Segmentation based on the Improved Particle Swarm Optimization Neural Networks
Chnese Word Segmentaton based on the Improved Partcle Swarm Optmzaton Neural Networks Ja He Computatonal Intellgence Laboratory School of Computer Scence and Engneerng, UESTC Chengdu, Chna Department of
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 informationHU Sheng-neng* Resources and Electric Power,Zhengzhou ,China
do:10.21311/002.31.6.09 Applcaton of new neural network technology n traffc volume predcton Abstract HU Sheng-neng* 1 School of Cvl Engneerng &Communcaton, North Chna Unversty of Water Resources and Electrc
More informationSHAPE RECOGNITION METHOD BASED ON THE k-nearest NEIGHBOR RULE
SHAPE RECOGNITION METHOD BASED ON THE k-nearest NEIGHBOR RULE Dorna Purcaru Faculty of Automaton, Computers and Electroncs Unersty of Craoa 13 Al. I. Cuza Street, Craoa RO-1100 ROMANIA E-mal: dpurcaru@electroncs.uc.ro
More informationNatural Computing. Lecture 13: Particle swarm optimisation INFR /11/2010
Natural Computng Lecture 13: Partcle swarm optmsaton Mchael Herrmann mherrman@nf.ed.ac.uk phone: 0131 6 517177 Informatcs Forum 1.42 INFR09038 5/11/2010 Swarm ntellgence Collectve ntellgence: A super-organsm
More informationApplication of Improved Fish Swarm Algorithm in Cloud Computing Resource Scheduling
, pp.40-45 http://dx.do.org/10.14257/astl.2017.143.08 Applcaton of Improved Fsh Swarm Algorthm n Cloud Computng Resource Schedulng Yu Lu, Fangtao Lu School of Informaton Engneerng, Chongqng Vocatonal Insttute
More informationStraight Line Detection Based on Particle Swarm Optimization
Sensors & ransducers 013 b IFSA http://www.sensorsportal.com Straght Lne Detecton Based on Partcle Swarm Optmzaton Shengzhou XU, Jun IE College of computer scence, South-Central Unverst for Natonaltes,
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 information(1) The control processes are too complex to analyze by conventional quantitative techniques.
Chapter 0 Fuzzy Control and Fuzzy Expert Systems The fuzzy logc controller (FLC) s ntroduced n ths chapter. After ntroducng the archtecture of the FLC, we study ts components step by step and suggest a
More informationKent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming
CS 4/560 Desgn and Analyss of Algorthms Kent State Unversty Dept. of Math & Computer Scence LECT-6 Dynamc Programmng 2 Dynamc Programmng Dynamc Programmng, lke the dvde-and-conquer method, solves problems
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 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 informationUsing Fuzzy Logic to Enhance the Large Size Remote Sensing Images
Internatonal Journal of Informaton and Electroncs Engneerng Vol. 5 No. 6 November 015 Usng Fuzzy Logc to Enhance the Large Sze Remote Sensng Images Trung Nguyen Tu Huy Ngo Hoang and Thoa Vu Van Abstract
More informationAPPLICATION OF IMPROVED K-MEANS ALGORITHM IN THE DELIVERY LOCATION
An Open Access, Onlne Internatonal Journal Avalable at http://www.cbtech.org/pms.htm 2016 Vol. 6 (2) Aprl-June, pp. 11-17/Sh Research Artcle APPLICATION OF IMPROVED K-MEANS ALGORITHM IN THE DELIVERY LOCATION
More informationFast Computation of Shortest Path for Visiting Segments in the Plane
Send Orders for Reprnts to reprnts@benthamscence.ae 4 The Open Cybernetcs & Systemcs Journal, 04, 8, 4-9 Open Access Fast Computaton of Shortest Path for Vstng Segments n the Plane Ljuan Wang,, Bo Jang
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 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 informationLoad Balancing for Hex-Cell Interconnection Network
Int. J. Communcatons, Network and System Scences,,, - Publshed Onlne Aprl n ScRes. http://www.scrp.org/journal/jcns http://dx.do.org/./jcns.. Load Balancng for Hex-Cell Interconnecton Network Saher Manaseer,
More informationADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM FOR SPEECH RECOGNITION THROUGH SUBTRACTIVE CLUSTERING
Internatonal Journal of Artfcal Intellgence & Applcatons (IJAIA), Vol. 5, No. 6, November 014 ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM FOR SPEECH RECOGNITION THROUGH SUBTRACTIVE CLUSTERING Samya Slarb
More informationAPPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT
3. - 5. 5., Brno, Czech Republc, EU APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT Abstract Josef TOŠENOVSKÝ ) Lenka MONSPORTOVÁ ) Flp TOŠENOVSKÝ
More informationFace Recognition Method Based on Within-class Clustering SVM
Face Recognton Method Based on Wthn-class Clusterng SVM Yan Wu, Xao Yao and Yng Xa Department of Computer Scence and Engneerng Tong Unversty Shangha, Chna Abstract - A face recognton method based on Wthn-class
More informationInvariant Shape Object Recognition Using B-Spline, Cardinal Spline, and Genetic Algorithm
Proceedngs of the 5th WSEAS Int. Conf. on Sgnal Processng, Robotcs and Automaton, Madrd, Span, February 5-7, 6 (pp4-45) Invarant Shape Obect Recognton Usng B-Splne, Cardnal Splne, and Genetc Algorthm PISIT
More informationRepeater Insertion for Two-Terminal Nets in Three-Dimensional Integrated Circuits
Repeater Inserton for Two-Termnal Nets n Three-Dmensonal Integrated Crcuts Hu Xu, Vasls F. Pavlds, and Govann De Mchel LSI - EPFL, CH-5, Swtzerland, {hu.xu,vasleos.pavlds,govann.demchel}@epfl.ch Abstract.
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 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 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 informationComputational Results of Hybrid Learning in Adaptive Neuro Fuzzy Inference System for Optimal Prediction
Internatonal Journal of Appled Engneerng Research ISSN 0973-456 Volume 1, Number 16 (017) pp. 5810-5818 Research Inda Publcatons. http://.rpublcaton.com Computatonal Results of Hybrd Learnng n Adaptve
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 informationOPTIMIZATION OF FUZZY RULE BASES USING CONTINUOUS ANT COLONY SYSTEM
Proceedng of the Frst Internatonal Conference on Modelng, Smulaton and Appled Optmzaton, Sharah, U.A.E. February -3, 005 OPTIMIZATION OF FUZZY RULE BASES USING CONTINUOUS ANT COLONY SYSTEM Had Nobahar
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 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 informationK-means Optimization Clustering Algorithm Based on Hybrid PSO/GA Optimization and CS validity index
Orgnal Artcle Prnt ISSN: 3-6379 Onlne ISSN: 3-595X DOI: 0.7354/jss/07/33 K-means Optmzaton Clusterng Algorthm Based on Hybrd PSO/GA Optmzaton and CS valdty ndex K Jahanbn *, F Rahmanan, H Rezae 3, Y Farhang
More informationAn efficient method to build panoramic image mosaics
An effcent method to buld panoramc mage mosacs Pattern Recognton Letters vol. 4 003 Dae-Hyun Km Yong-In Yoon Jong-Soo Cho School of Electrcal Engneerng and Computer Scence Kyungpook Natonal Unv. Abstract
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 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 informationProblem Set 3 Solutions
Introducton to Algorthms October 4, 2002 Massachusetts Insttute of Technology 6046J/18410J Professors Erk Demane and Shaf Goldwasser Handout 14 Problem Set 3 Solutons (Exercses were not to be turned n,
More information