Lecture Notes in Computer Science 3316Commenced Publication in 1973Founding and Former Series Editors:Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen

Editorial Board

David HutchisonLancaster University, UK

Takeo KanadeCarnegie Mellon University, Pittsburgh, PA, USA

Josef KittlerUniversity of Surrey, Guildford, UK

Jon M. KleinbergCornell University, Ithaca, NY, USA

Friedemann MatternETH Zurich, Switzerland

John C. MitchellStanford University, CA, USA

Moni NaorWeizmann Institute of Science, Rehovot, Israel

Oscar NierstraszUniversity of Bern, Switzerland

C. Pandu RanganIndian Institute of Technology, Madras, India

Bernhard SteffenUniversity of Dortmund, Germany

Madhu SudanMassachusetts Institute of Technology, MA, USA

Demetri TerzopoulosNew York University, NY, USA

Doug TygarUniversity of California, Berkeley, CA, USA

Moshe Y. VardiRice University, Houston, TX, USA

Gerhard WeikumMax-Planck Institute of Computer Science, Saarbruecken, Germany

Nikhil R. Pal Nikola KasabovRajani K. Mudi Srimanta PalSwapan K. Parui (Eds.)

NeuralInformation Processing

11th International Conference, ICONIP 2004Calcutta, India, November 22-25, 2004Proceedings

1 3

Volume Editors

Nikhil R. PalSrimanta PalIndian Statistical InstituteElectronics and Communications Sciences Unit203 B. T. Road, Calcutta 700 108, IndiaE-mail: {nikhil,srimanta}@isical.ac.in

Nikola KasabovAuckland University of TechnologyKnowledge Engineering and Discovery Research Institute (KEDRI)Private Bag 92006, Auckland, New ZealandE-mail: nik.kasabov@aut.ac.nz

Rajani K. MudiJadavpur UniversityDepartment of Instrumentation and Electronics EngineeringSalt-lake Campus, Calcutta 700098, IndiaE-mail: rkmudi@iee.jusl.ac.in

Swapan K. ParuiIndian Statistical InstituteComputer Vision and Pattern Recognition Unit203 B. T. Road, Calcutta 700 108, IndiaE-mail: swapan@isical.ac.in

Library of Congress Control Number: 2004115128

CR Subject Classification (1998): F.1, I.2, I.5, I.4, G.3, J.3, C.2.1, C.1.3, C.3

ISSN 0302-9743ISBN 3-540-23931-6 Springer Berlin Heidelberg New York

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Preface

It is our great pleasure to welcome you to the 11th International Conference onNeural Information Processing (ICONIP 2004) to be held in Calcutta. ICONIP2004 is organized jointly by the Indian Statistical Institute (ISI) and JadavpurUniversity (JU). We are confident that ICONIP 2004, like the previous confer-ences in this series, will provide a forum for fruitful interaction and the exchangeof ideas between the participants coming from all parts of the globe. ICONIP2004 covers all major facets of computational intelligence, but, of course, witha primary emphasis on neural networks. We are sure that this meeting will beenjoyable academically and otherwise.

We are thankful to the track chairs and the reviewers for extending theirsupport in various forms to make a sound technical program. Except for a fewcases, where we could get only two review reports, each submitted paper wasreviewed by at least three referees, and in some cases the revised versions wereagain checked by the referees. We had 470 submissions and it was not an easy taskfor us to select papers for a four-day conference. Because of the limited durationof the conference, based on the review reports we selected only about 40% ofthe contributed papers. Consequently, it is possible that some good papers areleft out. We again express our sincere thanks to all referees for accomplishing agreat job. In addition to 186 contributed papers, the proceedings includes twoplenary presentations, four invited talks and 18 papers in four special sessions.The proceedings is organized into 26 coherent topical groups. We are proud tohave a list of distinguished speakers including Profs. S. Amari, W.J. Freeman,N. Saitou, L. Chua, R. Eckmiller, E. Oja, and T. Yamakawa. We are happy tonote that 27 different countries from all over the globe are represented by theauthors, thereby making it a truly international event.

We are grateful to Prof. A.N. Basu, Vice-Chancellor, JU and Prof. K.B.Sinha, Director, ISI, who have taken special interest on many occasions to helpthe organizers in many ways and have supported us in making this conference areality. Thanks are due to the Finance Chair, Prof. R. Bandyopadhyay, and theTutorial Chair, Prof. B.B. Chaudhuri.

We want to express our sincere thanks to the members of the Advisory Com-mittee for their timely suggestions and guidance. We sincerely acknowledge thewholehearted support provided by the members of the Organizing Committee.Special mention must be made of the organizing Co-chairs, Prof. D. Patranabisand Prof. J. Das for their initiative, cooperation, and leading roles in organiz-ing the conference. The staff members of the Electronics and CommunicationSciences Unit of ISI have done a great job and we express our thanks to them.We are also grateful to Mr. Subhasis Pal of the Computer and Statistical Ser-vices Center, ISI, for his continuous support. Things will remain incompleteunless we mention Mr. P.P. Mohanta, Mr. D. Chakraborty, Mr. D.K. Gayenand Mr. S.K. Shaw without whose help it would have been impossible for us to

VI Preface

make this conference a success. We must have missed many other colleagues andfriends who have helped us in many ways; we express our sincere thanks to themalso.

We gratefully acknowledge the financial support provided by different organi-zations, as listed below. Their support helped us greatly to hold this conferenceon this scale.

Last, but surely not the least, we express our sincere thanks to Mr. AlfredHofmann and Ms. Ursula Barth of Springer for their excellent support in bring-ing out the proceedings on time.

November 2004 Nikhil R. PalNikola KasabovRajani K. Mudi

Srimanta PalSwapan K. Parui

Funding Agencies

• Infosys Technologies Limited, India• IBM India Research Lab, India• Department of Science and Technology, Govt. of India• Council of Scientific and Industrial Research, Govt. of India• Reserve Bank of India• Department of Biotechnology, Govt. of India• Defence Research and Development Organization, Govt. of India• Department of Higher Education, Govt. of West Bengal, India• Jadavpur University, Calcutta, India• Indian Statistical Institute, Calcutta, India

Organization

OrganizersIndian Statistical Institute, Calcutta, IndiaJadavpur University, Calcutta, IndiaComputational Intelligence Society of India (CISI), India

Chief PatronsK.B. Sinha, Indian Statistical Institute, IndiaA.N. Basu, Jadavpur University, India

Honorary Co-chairsS. Amari, Riken Brain Science Institute, JapanT. Kohonen, Neural Networks Research Centre, Finland

General ChairN.R. Pal, Indian Statistical Institute, India

Vice Co-chairsE. Oja, Helsinki University of Technology, FinlandR. Krishnapuram, IBM India Research Lab, India

Program ChairN. Kasabov, University of Otago, New Zealand

Organizing Co-chairsD. Patranabis, Jadavpur University, IndiaJ. Das, Indian Statistical Institute, India

Joint SecretariesS.K. Parui, Indian Statistical Institute, IndiaR.K. Mudi, Jadavpur University, IndiaS. Pal, Indian Statistical Institute, India

Tutorials ChairB.B. Chaudhuri, Indian Statistical Institute, India

Finance ChairR. Bandyopadhyay, Jadavpur University, India

Technical SponsorsAsia Pasific Neural Network Assembly (APNNA)World Federation on Soft Computing (WFSC)

VIII Organization

Advisory Committee

A. Atiya, Cairo University, EgyptMd.S. Bouhlel, National Engineering School of Sfax, TunisiaS. Chakraborty, Institute of Engineering and Management, IndiaG. Coghill, University of Auckland, New ZealandA. Engelbrecht, University of Pretoria, South AfricaD. Fogel, Natural Selection Inc., USAK. Fukushima, Tokyo University of Technology, JapanT. Gedeon, Australian National University, AustraliaL. Giles, NEC Research Institute, USAM. Gori, Universita di Siena, ItalyR. Hecht-Nielsen, University of California, USAW. Kanarkard, Ubonratchathani University, ThailandO. Kaynak, Bogazici University, TurkeyS.V. Korobkova, Scientific Centre of Neurocomputers, RussiaS.Y. Lee, Korea Advanced Institute of Science and Technology, KoreaC.T. Lin, National Chiao-Tung University, TaiwanD.D. Majumder, Indian Statistical Institute, IndiaM. Mitra, Jadavpur University, IndiaD. Moitra, Infosys Technologies Limited, IndiaL.M. Patnaik, Indian Institute of Science, IndiaW. Pedrycz, University of Alberta, CanadaS.B. Rao, Indian Statistical Institute, IndiaV. Ravindranath, National Brain Research Centre, IndiaJ. Suykens, Katholieke Universiteit Leuven, BelgiumA.R. Thakur, Jadavpur University, IndiaS. Usui, Neuroinformatics Lab., RIKEN BSI, JapanL. Wang, Nanyang Technological University, SingaporeL. Xu, Chinese University of Hong Kong, Hong KongT. Yamakawa, Kyushu Institute of Technology, JapanY.X. Zhong, University of Posts and Telecommunications, ChinaJ. Zurada, University of Louisville, USA

Organization IX

Track Chairs

Quantum Computing E. Behrman (USA)Bayesian Computing Z. Chan (New Zealand)Bio-informatics J.Y. Chang (Taiwan)Support Vector Machines V.S. Cherkassky (USA)

and Kernel MethodsBiometrics S.B. Cho (Korea)Fuzzy, Neuro-fuzzy and F.K. Chung (Hong Kong)

Other Hybrid SystemsTime Series Prediction and W. Duch (Poland)

Data AnalysisEvolutionary Computation T. Furuhashi (Japan)Neuroinformatics I. Hayashi (Japan)Pattern Recognition R. Kothari (India)Control Systems T.T. Lee (Taiwan)Image Processing and Vision M.T. Manry (USA)

Robotics J.K. Mukherjee (India)Novel Neural Network M. Palaniswami (Australia)

ArchitecturesBrain Study Models V. Ravindranath (India)Brain-like Computing A. Roy (USA)Learning Algorithms P.N. Suganthan (Singapore)Cognitive Science R. Sun (USA)Speech and Signal Processing H. Szu (USA)Computational Neuro-science S. Usui (Japan)Neural Network Hardware T. Yamakawa (Japan)

Organizing Committee

K. BanerjeeJ. BasakU. BhattacharyaB.B. BhattacharyaB. ChandaN. ChatterjeeB.N. ChatterjiB. DamA.K. DeK. DebU.B. DesaiB.K. Dutta

U. GaraiKarmeshuR. KothariS. KumarK. MadhanmohanK. MajumdarA.K. MandalM. MitraD.P. MukherjeeK. MukhopadhyayP.K. NandiU. Pal

P. PalS. RahaBaldev RajK. RayK.S. RayB.K. RoyK.K. ShuklaB.P. SinhaB. Yegnanarayana

X Organization

Reviewers

S. AbeA. AbrahamM. AlcyA.S. Al-HegamiN.M. AllinsonE. AlpaydinL. AndrejA. ArsenioA. AtiyaM. AtsumiM. AzimT. BalachanderJ. BasakY. BecerikliR. BeggL. BeheraY. BengioU. BhattacharyaC. BhattacharyyaA.K. BhaumikA. BiemZ. BingulS. BiswasS.N. BiswasM. BlumensteinS. BuchalaA. CanutoA.C. CarcamoF.A.T. CarvahoK.-M. ChaD. ChakrabortyDebrup ChakrabortyS. ChakrabortyU. ChakrabortyC.-H. ChanB. ChandaH. ChandrasekaranK. ChangB.N. ChatterjeeB.B. ChaudhuriH.-H. ChenJ. ChenK. ChenT.K. Chen

V. CherkasskyW. CheungE. ChoS. ChoS.B. ChoK. ChokshiN. ChowdhuryA.A. CohenR.F. CorreaS. DamingJ. DasN. DasC.A. DavidA.K. DeyR. DeK. DebW.H. DelashmitG. DengB. DharaG. DimitrisM. DongY. DongT. DownsK. DoyaW. DuchA. DuttaD.P.W. EllisM. ErP. E’rdiM. ErtuncA. EspositoE.C. EugeneX. FanZ.-G. FanO. FarooqS. FranklinM. FukuiT. FuruhashiM. GabreaM. GallagherU. GarainA. GarcezS.S. GeT. Gedeon

T.V. GeethaA. GhoshS. GhoshB.G. GibsonK. GopalsamyK.D. GopichandR. GoreM. GranaL. GuanC. GuangH. GuangbinA. HafezM. HagiwaraM. HattoriI. HayashiG. HeidemannG.Z. ChiS. HimavathiP.S. HiremathA. HiroseL. HongtaoC.-H. HsiehW.-HsuB. HuangH.-D. HuangY.K. HuiM.F. HussinS. IkedaT. InoueH. IshibuchiP. JaganathanM. JalilianM. Jalili-KharaajooG. JiX. JiangL. JinyanT. KalyaniT. KambaraC.-Y. KaoK. KarimN. KasabovU. KaymakO. KaynakS.S. Khan

Organization XI

J.Y. KiJ. KimJ.-H. KimK. KimK.B. KimH. KitaA. KoenigM. KoppenK. KotaniR. KothariR. KozmaK. KrishnaR. KrishnapuramS.N. KudohC. KuiyuA. KumarA.P. KumarS. KumarM.K. KunduY. KuroeS. KurogiJ. KwokH.Y. KwonJ. LaaksonenS. LaConteA. LahaD. LaiS. LaineR. LangariJ.-H. LeeK.J. LeeV.C.S. LeeJ. LiP. LiS. LianC. LihuiC.-J. LinC.T. LinC. LiuJ. LiuP. LokugeR. LotlikarM. LouwerseC.L. LuT. Ludermir

C.K. LukP.-C. LyuY. MaedaS. MaitraS.P. MaityK. MajumdarM. MakF.J. MaldonadoA.K. MandalJ. MandziukN. ManiD.H. ManjaiahM. MatsuguY. MatsuyamaB. McKayO. MinM. MitraS. MitraB.M. MohanP.P. MohantaR.K. MudiS. MukherjeaA. MukherjeeD.P. MukherjeeJ.K. MukherjeeK. MukhopadhyayaA.K. MuslaW. NaeemP. NagabhushanH. NajafiT. NakashimaP.K. NandaP. NarasimhaM. NasipuraV.S. NathanG.S. NgA. NijholtG.S. NimD. NoelleA. OgawaE. OjaH. OkamotoP.R. OliveiraT. OmoriY. Oysal

G.A.V. PaiN.R. PalS. PalU. PalS. PalitR. PanchalJ.-A. ParkK.R. ParkS.K. ParuiM.S. PatelS.K. PatraM. PerusT.D. PhamA.T.L. PhuanH. PilevarM. PremaratneS. PuthusserypadyS. QingM. RajeswariK.S. RaoF. RashidiM. RashidiV. RavindranathB.K. RoutA. RoyP.K. RoyR. RoyJ. Ruiz-del-SolarS. SahaS. SahariaA.D. SahasrabudheS. SahinJ.S. SahmbiM. SakalliA.R. SaravananS.N. SarbadhikariP. SarkarP.S. SastryA. SavranH. SawaiA. SaxenaC.C. SekharA. SharmaC. ShawB.H. Shekar

XII Organization

P.K. ShettyZ. ShiC.N. ShivajiP. ShivakumaraK.K. ShuklaA.P. Silva LinsM.J. Silva ValencaJ.K. SingR. SinghS. SinghS. SinhaM. SirolaG. SitaK.R. SivaramakrishnanJ. SjobergK. SmithX. SongM.C.P. de SoutoA. SowmyaR. SrikanthB. SrinivasanP.N. SuganthanC. SunR. SunE. SungV. SureshJ. Suykens

R. TadeusiewiczP.K.S. TamH. TamakiC.Y. TangE.K. TangP. ThompsonK.-A. TohA. TordaV. TorraD. TranJ. UchidaS. UsuiP. VadakkepatB. ValsaD. VenturaA. VermaB. VermaJ. VesantoE. VijayakumarD. WangJ. WangL. WangS. WangJ. WatadaO. WatanabeJ. WeiA. Wichert

R.H.S. WongK.-W. Wong (Kevin)B. XiaC. YamaguchiY. YamaguchiT. YamakawaL. YaoS. YasuiZ. YehD.C.S. YeungH. YigitC.-G. YooN.M. YoungC. YuM. YunqianC. ZanchettinZ. Zenn BienB.-T. ZhangD. ZhangL. ZhangQ. ZhangY. ZhangL. ZhiyingS. ZhongD. ZhouH. Zujun

Table of Contents

Computational Neuroscience

Neurobiological Foundation for the Meaning of Information . . . . . . . . . . . . . 1Walter J. Freeman

Neural Information Processing Efforts to Restore Vision in the Blind . . . . . 10Rolf Eckmiller, Oliver Baruth, and Dirk Neumann

Synchronous Phenomena for Two-Layered Neural Networkwith Chaotic Neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Katsuki Katayama, Masafumi Yano, and Tsuyoshi Horiguchi

Influence of Dendritic Spine Morphology on Spatiotemporal Changeof Calcium/Calmoduline-Dependent Protein Kinase Density . . . . . . . . . . . . . 31

Shuichi Kato, Seiichi Sakatani, and Akira Hirose

Memory Modification Induced by Pattern Completion and STDPin Hippocampal CA3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Toshikazu Samura and Motonobu Hattori

Neural Mechanism of Binding ITD Information with IID Onefor Generating Brain Map of Sound Localization . . . . . . . . . . . . . . . . . . . . . . . 44

Kazuhisa Fujita, ShungQuang Huang, Yoshiki Kashimori,and Takeshi Kambara

The Spatiotemporal Dynamicsof Intracellular Ion Concentration and Potential . . . . . . . . . . . . . . . . . . . . . . . . 50

Seiichi Sakatani and Akira Hirose

A Model That Captures Receptive Field Propertiesof Orientation Selective Neurons in the Visual Cortex . . . . . . . . . . . . . . . . . . . 57

Basabi Bhaumik, Alok Agarwal, Mona Mathur, and Manish Manohar

Development of a Simple Cell Receptive Field Structure:A Model Based on Hetero-synaptic Interactions . . . . . . . . . . . . . . . . . . . . . . . . 64

Akhil R. Garg, Basabi Bhaumik, and Klaus Obermayer

The Role of the Basal Ganglia in Exploratory Behaviorin a Model Based on Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Sridharan Devarajan, P.S. Prashanth, and V.S. Chakravarthy

A Functional Role of FM Sweep Rate of Biosonar in Echolocation of Bat . . 78Kazuhisa Fujita, Eigo Kamata, Satoru Inoue, Yoshiki Kashimori,and Takeshi Kambara

XIV Table of Contents

Orientation Map Emerges in Parallel with the Formationof Receptive Fields in a Feedforward Neurotrophic Model . . . . . . . . . . . . . . . 84

Mona Mathur and Basabi Bhaumik

The Balance Between Excitation and InhibitionNot Only Leads to Variable Discharge of Cortical Neuronsbut Also to Contrast Invariant Orientation Tuning . . . . . . . . . . . . . . . . . . . . . 90

Akhil R. Garg, Basabi Bhaumik, and Klaus Obermayer

Stochastic Resonance Imaging – Stochastic Resonance Therapy:Preliminary Studies Considering Brain as Stochastic Processor . . . . . . . . . . . 96

Prasun Kumar Roy

Complex-Valued Neural Networks

Ultra-wideband Beamformingby Using a Complex-Valued Spatio-temporal Neural Network . . . . . . . . . . . . 104

Andriyan B. Suksmono and Akira Hirose

A Model of Hopfield-Type Quaternion Neural Networksand Its Energy Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

Mitsuo Yoshida, Yasuaki Kuroe, and Takehiro Mori

Mode-Utilizing Developmental LearningBased on Coherent Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

Akira Hirose, Yasufumi Asano, and Toshihiko Hamano

Dynamics of Complex-Valued Neural Networksand Its Relation to a Phase Oscillator System . . . . . . . . . . . . . . . . . . . . . . . . . 122

Ikuko Nishikawa and Yasuaki Kuroe

Two Models for Theta Precession Generation Using the Complex Versionof the Nagumo-Sato Neuron Model and the Hodgkin-Huxley Equations . . . 130

Iku Nemoto

Self-organizing Maps

Using Self-organizing Map in a Computerized Decision Support System . . . 136Miki Sirola, Golan Lampi, and Jukka Parviainen

An Empirical Study on the Robustness of SOM in Preserving Topologywith Respect to Link Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

Arijit Laha

Extending the SOM Algorithm to Non-Euclidean Distancesvia the Kernel Trick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

Manuel Martın-Merino and Alberto Munoz

Table of Contents XV

An Efficient Two-Level SOMART Document ClusteringThrough Dimensionality Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

Mahmoud F. Hussin, Mohamed S. Kamel, and Magdy H. Nagi

Color Image Vector Quantization Using Wavelet Transformand Enhanced Self-organizing Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . 166

Kwang Baek Kim and Dae Su Kim

Using SOM-Based Data Binningto Support Supervised Variable Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172

Sampsa Laine and Timo Simila

Evolutionary Computation

Packing Bins Using Multi-chromosomal Genetic Representationand Better-Fit Heuristic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

A.K. Bhatia and S.K. Basu

Data Association for Multiple Target Tracking:An Optimization Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

Mukesh A. Zaveri, S.N. Merchant, and Uday B. Desai

Expected Running Time Analysis of a MultiobjectiveEvolutionary Algorithm on Pseudo-boolean Functions . . . . . . . . . . . . . . . . . . 193

Nilanjan Banerjee and Rajeev Kumar

The Influence of Gaussian, Uniform, and Cauchy Perturbation Functionsin the Neural Network Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

Paulito P. Palmes and Shiro Usui

Closest Substring Problem – Results from an Evolutionary Algorithm . . . . 205Holger Mauch

Quantum-Inspired Evolutionary Algorithmsand Its Application to Numerical Optimization Problems . . . . . . . . . . . . . . . 212

Andre V. Abs da Cruz, Carlos R. Hall Barbosa,Marco Aurelio C. Pacheco, and Marley Vellasco

Multiobjective Genetic Search for Spanning Tree Problem . . . . . . . . . . . . . . . 218Rajeev Kumar, P.K. Singh, and P.P. Chakrabarti

A Partheno-genetic Algorithm for Combinatorial Optimization . . . . . . . . . . . 224Maojun Li, Shaosheng Fan, and An Luo

Evaluation of Comprehensive Learning Particle Swarm Optimizer . . . . . . . . 230Jing J. Liang, A. Kai Qin, Ponnuthurai Nagaratnam Suganthan,and S. Baskar

XVI Table of Contents

Evolutionary Learning Program’s Behavior in Neural Networksfor Anomaly Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236

Sang-Jun Han, Kyung-Joong Kim, and Sung-Bae Cho

Gray and Binary Encoding in the (1+1)-EA . . . . . . . . . . . . . . . . . . . . . . . . . . . 242Uday K. Chakraborty

Control Systems

Asymptotic Stability of Nonautonomous Delayed Neural Networks . . . . . . . 248Qiang Zhang, Xiaopeng Wei, Jin Xu, and Dongsheng Zhou

A New PID Tuning Technique Using Differential Evolutionfor Unstable and Integrating Processes with Time Delay . . . . . . . . . . . . . . . . 254

Zafer Bingul

Representation and Identification of Finite State Automataby Recurrent Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261

Yasuaki Kuroe

Neural Network Closed-Loop ControlUsing Sliding Mode Feedback-Error-Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 269

Andon V. Topalov and Okyay Kaynak

State Estimation and Tracking Problems: A ComparisonBetween Kalman Filter and Recurrent Neural Networks . . . . . . . . . . . . . . . . . 275

S. Kumar Chenna, Yogesh Kr. Jain, Himanshu Kapoor, Raju S. Bapi,N. Yadaiah, Atul Negi, V. Seshagiri Rao, and B.L. Deekshatulu

Cognitive Science

A Connectionist Account of Ontological Boundary Shifting . . . . . . . . . . . . . . 282Shohei Hidaka and Jun Saiki

A Neural Network Model for Trace Conditioning . . . . . . . . . . . . . . . . . . . . . . . 288Tadashi Yamazaki and Shigeru Tanaka

Chunking Phenomenon in Complex Sequential Skill Learning in Humans . . 294V.S. Chandrasekhar Pammi, K.P. Miyapuram, Raju S. Bapi,and Kenji Doya

Cognitive Process of Emotion Under Uncertainty . . . . . . . . . . . . . . . . . . . . . . 300Ayako Onzo and Ken Mogi

The Locus of Word Length and Frequency Effect in ComprehendingEnglish Words by Korean-English Bilinguals and Americans . . . . . . . . . . . . . 306

Kichun Nam, Yoonhyong Lee, and Chang H. Lee

Table of Contents XVII

Cerebral Activation Areas with Respect to Word and Sentence Productionby Early and Late Korean-English Bilinguals:Event-Related fMRI Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316

Choong-Myung Kim, Donghoon Lee, and Kichun Nam

Biometrics

Fusion of Dimension Reduction Methodsand Application to Face Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321

Byungjun Son, Sungsoo Yoon, and Yillbyung Lee

A Hardware-Directed Face Recognition SystemBased on Local Eigen-analysis with PCNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327

C. Siva Sai Prasanna, N. Sudha, and V. Kamakoti

The Teager Energy Based Features for Identification of Identical Twinsin Multi-lingual Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333

Hemant A. Patil and T.K. Basu

A Fast and Efficient Face Detection TechniqueUsing Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338

R. Suguna, N. Sudha, and C. Chandra Sekhar

User Enrollment Using Multiple Snapshots of Fingerprint . . . . . . . . . . . . . . . 344Younhee Gil, Dosung Ahn, Choonwoo Ryu, Sungbum Pan,and Yongwha Chung

Signature Verification Using Static and Dynamic Features . . . . . . . . . . . . . . . 350Mayank Vatsa, Richa Singh, Pabitra Mitra, and Afzel Noore

Face Recognition Using SVM Combined with CNN for Face Detection . . . . 356Masakazu Matsugu, Katsuhiko Mori, and Takashi Suzuki

Face Recognition Using Weighted ModularPrinciple Component Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362

A. Pavan Kumar, Sukhendu Das, and V. Kamakoti

Adaptive Intelligent Systems

Self-organizing Relationship (SOR) Network with Fuzzy Inference BasedEvaluation and Its Application to Trailer-Truck Back-Up Control . . . . . . . . 368

Takanori Koga, Keiichi Horio, and Takeshi Yamakawa

In-vehicle Noise and Enhanced Speech Intelligibility . . . . . . . . . . . . . . . . . . . . 375Akbar Ghobakhlou and Richard Kilgour

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An Evolving Neural Network Model for Person VerificationCombining Speech and Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381

Akbar Ghobakhlou, David Zhang, and Nikola Kasabov

Adaptive Affine Subspace Self-organizing Map with Kernel Method . . . . . . 387Hideaki Kawano, Keiichi Horio, and Takeshi Yamakawa

Brain-Like Computing

Scene Memory on Competitively Growing Neural NetworkUsing Temporal Coding: Self-organized Learningand Glance Recognizability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393

Masayasu Atsumi

Pulsed Para-neural Networks (PPNN) Based on MEXOR Logic . . . . . . . . . . 399Andrzej Buller, Ismail Ahson, and Muzaffar Azim

Knowledge Reusing Neural Learning Systemfor Immediate Adaptation in Navigation Tasks . . . . . . . . . . . . . . . . . . . . . . . . 409

Akitoshi Ogawa and Takashi Omori

Universal Spike-Train Processor for a High-Speed Simulationof Pulsed Para-neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416

Michal Joachimczak, Beata Grzyb, and Daniel Jelinski

Knowledge Extraction from Artificial Associative Memoryfor Helping Senile Dementia Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422

JeongYon Shim

Learning Algorithms

Some Experiments on Training Radial Basis Functionsby Gradient Descent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428

Mercedes Fernandez-Redondo, Carlos Hernandez-Espinosa,Mamen Ortiz-Gomez, and Joaquın Torres-Sospedra

Predictive Approaches for Sparse Model Learning . . . . . . . . . . . . . . . . . . . . . . 434S.K. Shevade, S. Sundararajan, and S.S. Keerthi

Multiple Instance Learning with Radial Basis Function Neural Networks . . 440Abdelhamid Bouchachia

Leverages Based Neural Networks Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446Antanas Verikas, Marija Bacauskiene, and Adas Gelzinis

A Process of Differentiation in the Assembly Neural Network . . . . . . . . . . . . 452Alexander Goltsev, Ernst Kussul, and Tatyana Baidyk

Table of Contents XIX

Managing Interference Between Prior and Later Learning . . . . . . . . . . . . . . . 458L. Andrew Coward, Tamas D. Gedeon, and Uditha Ratnayake

A Neural Learning Rule for CCA Approximation . . . . . . . . . . . . . . . . . . . . . . 465M. Shahjahan and K. Murase

Adaptive Learning in Incremental Learning RBF Networks . . . . . . . . . . . . . . 471T.N. Nagabhushan and S.K. Padma

Recurrent Neural Networksfor Learning Mixed kth-Order Markov Chains . . . . . . . . . . . . . . . . . . . . . . . . . 477

Wang Xiangrui and Narendra S. Chaudhari

An Efficient Generalization of Battiti-Shanno’s Quasi-Newton Algorithmfor Learning in MLP-Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483

Carmine Di Fiore, Stefano Fanelli, and Paolo Zellini

Incremental Learning and Dimension Selection Through Sleep . . . . . . . . . . . 489Koichiro Yamauchi

The Most Robust Loss Function for Boosting . . . . . . . . . . . . . . . . . . . . . . . . . . 496Takafumi Kanamori, Takashi Takenouchi, Shinto Eguchi,and Noboru Murata

An On-Line Learning Algorithm with Dimension SelectionUsing Minimal Hyper Basis Function Networks . . . . . . . . . . . . . . . . . . . . . . . 502

Kyosuke Nishida, Koichiro Yamauchi, and Takashi Omori

Density Boosting for Gaussian Mixtures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508Xubo Song, Kun Yang, and Misha Pavel

Improving kNN Based Text Classificationwith Well Estimated Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516

Heui Seok Lim

One-Epoch Learning for Supervised Information-TheoreticCompetitive Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524

Ryotaro Kamimura

Teacher-Directed Learning with Gaussianand Sigmoid Activation Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 530

Ryotaro Kamimura

Gradient Type Learning Rules for Neural NetworksBased on Watcher-Environment Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537

M. Tanvir Islam and Yoichi Okabe

Variational Information Maximization for Neural Coding . . . . . . . . . . . . . . . . 543Felix Agakov and David Barber

XX Table of Contents

Comparison of TDLeaf(λ) and TD(λ) Learningin Game Playing Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549

Daniel Osman and Jacek Mandziuk

Rule Extraction by Seeing Through the Model . . . . . . . . . . . . . . . . . . . . . . . . . 555Tuve Lofstrom, Ulf Johansson, and Lars Niklasson

An Auxiliary Variational Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561Felix V. Agakov and David Barber

Gaussian Process Regression with Fluid Hyperpriors . . . . . . . . . . . . . . . . . . . 567Ramunas Girdziusas and Jorma Laaksonen

Learning Team Cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573Ron Sun and Dehu Qi

Training Minimal Uncertainty Neural Networks by Bayesian Theoremand Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579

Yan Wang, Chun-Guang Zhou, Yan-Xin Huang, and Xiao-Yue Feng

A Forward-Propagation Rule for Acquiring Neural Inverse ModelsUsing a RLS Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585

Yoshihiro Ohama, Naohiro Fukumura, and Yoji Uno

Generalization in Learning Multiple Temporal Patterns Using RNNPB . . . 592Masato Ito and Jun Tani

Structural Learning of Neural Network for Continuous Valued Output:Effect of Penalty Term to Hidden Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 599

Basabi Chakraborty and Yusuke Manabe

Argumentation Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 606Artur d’Avila Garcez, Dov Gabbay, and Luıs C. Lamb

A Neighbor Generation Mechanism Optimizing Neural Networks . . . . . . . . . 613Amanda Lins and Teresa Ludermir

Collaborative Agent Learning Using Neurocomputing . . . . . . . . . . . . . . . . . . . 619Saulat Farooque, Ajith Abraham, and Lakhmi Jain

Novel Neural Networks

Cognitive Routing in Packet Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625Erol Gelenbe

TWRBF – Transductive RBF Neural Networkwith Weighted Data Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633

Qun Song and Nikola Kasabov

Table of Contents XXI

An Incremental Neural Networkfor Non-stationary Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641

Shen Furao and Osamu Hasegawa

Computing Convex-Layersby a Multi-layer Self-organizing Neural Network . . . . . . . . . . . . . . . . . . . . . . . 647

Amitava Datta and Srimanta Pal

Cost-Sensitive Greedy Network-Growing Algorithmwith Gaussian Activation Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653

Ryotaro Kamimura and Osamu Uchida

Image Processing

An Efficient Skew Estimation Technique for Binary Document ImagesBased on Boundary Growing and Linear Regression Analysis . . . . . . . . . . . . 659

P. Shivakumara, G. Hemantha Kumar, D.S. Guru,and P. Nagabhushan

Segmenting Moving Objectswith a Recurrent Stochastic Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . 666

Jieyu Zhao

Real-Time Gaze Detection via Neural Network . . . . . . . . . . . . . . . . . . . . . . . . 673Kang Ryoung Park

CA Based Document Compression Technology . . . . . . . . . . . . . . . . . . . . . . . . . 679Chandrama Shaw, Biplab K. Sikdar, and N.C. Maiti

Size-Independent Image Segmentation by Hierarchical Clusteringand Its Application for Face Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 686

Motofumi Fukui, Noriji Kato, Hitoshi Ikeda, and Hirotsugu Kashimura

Human-Like Selective Attention Modelwith Reinforcement and Inhibition Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . 694

Sang-Bok Choi, Sang-Woo Ban, and Minho Lee

Genetic Algorithm for Optimal Imperceptibility in Image CommunicationThrough Noisy Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 700

Santi P. Maity, Malay K. Kundu, and Prasanta K. Nandi

High Speed Extraction Model of ROI for Automatic Logistics System . . . . . 706Moon-sung Park, Il-sook Kim, Eun-kyung Cho, and Young-hee Kwon

Using Biased Support Vector Machine to Improve Retrieval Resultin Image Retrieval with Self-organizing Map . . . . . . . . . . . . . . . . . . . . . . . . . . . 714

Chi-Hang Chan and Irwin King

XXII Table of Contents

A Fast MPEG4 Video Encryption SchemeBased on Chaotic Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720

Shiguo Lian, Jinsheng Sun, Zhongxin Li, and Zhiquan Wang

Content-Based Video Classification Using Support Vector Machines . . . . . . 726Vakkalanka Suresh, C. Krishna Mohan, R. Kumara Swamy,and B. Yegnanarayana

Fast Half Pixel Motion EstimationBased on Spatio-temporal Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 732

HyoSun Yoon, GueeSang Lee, SooHyung Kim, and Deokjai Choi

Pattern Recognition

Local and Recognizable Iso Picture Languages . . . . . . . . . . . . . . . . . . . . . . . . . 738T. Kalyani, V.R. Dare, and D.G. Thomas

Multilayer Feedforward Ensembles for Classification Problems . . . . . . . . . . . 744Mercedes Fernandez-Redondo, Carlos Hernandez-Espinosa,and Joaquın Torres-Sospedra

Performance Advantage of Combined Classifiers in Multi-category Cases:An Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 750

Xubo Song and Misha Pavel

Web Documents Categorization Using Neural Networks . . . . . . . . . . . . . . . . . 758Renato Fernandes Correa and Teresa Bernarda Ludermir

Gender Classification of Face Images:The Role of Global and Feature-Based Information . . . . . . . . . . . . . . . . . . . . . 763

Samarasena Buchala, Neil Davey, Ray J. Frank, Tim M. Gale,Martin J. Loomes, and Wanida Kanargard

Classification of SAR ImagesThrough a Convex Hull Region Oriented Approach . . . . . . . . . . . . . . . . . . . . . 769

Simith T. D’Oliveira Junior, Francisco de A.T. de Carvalho,and Renata M.C.R. de Souza

Clustering of Interval-Valued DataUsing Adaptive Squared Euclidean Distances . . . . . . . . . . . . . . . . . . . . . . . . . . 775

Renata M.C.R. de Souza, Francisco de A.T. de Carvalho,and Fabio C.D. Silva

A Two-Pass Approach to Pattern Classification . . . . . . . . . . . . . . . . . . . . . . . . 781Subhadip Basu, C. Chaudhuri, Mahantapas Kundu, Mita Nasipuri,and Dipak Kumar Basu

Table of Contents XXIII

A Long Memory Process Based Parametric Modeling and Recognitionof PD Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 787

Pradeep Kumar Shetty

A Fusion of Neural Network Based Auto-associator and Classifierfor the Classification of Microcalcification Patterns . . . . . . . . . . . . . . . . . . . . . 794

Rinku Panchal and Brijesh Verma

Time Series Classificationfor Online Tamil Handwritten Character Recognition –A Kernel Based Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 800

K.R. Sivaramakrishnan and Chiranjib Bhattacharyya

Tamil Handwriting RecognitionUsing Subspace and DTW Based Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . 806

Niranjan Joshi, G. Sita, A.G. Ramakrishnan,and Sriganesh Madhvanath

Recognition of Bangla Handwritten CharactersUsing an MLP Classifier Based on Stroke Features . . . . . . . . . . . . . . . . . . . . . 814

T.K. Bhowmik, U. Bhattacharya, and Swapan K. Parui

Elastic Matching Algorithms for Online Tamil Character Recognition . . . . . 820Niranjan Joshi, G. Sita, A.G. Ramakrishnan,and Sriganesh Madhvanath

Automated Classification of Industry and Occupation CodesUsing Document Classification Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 827

Heui Seok Lim and Hyeoncheol Kim

Abnormality Detection in Endoscopic Images Using Color Segmentationand Curvature Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 834

P.S. Hiremath, B.V. Dhandra, Ravindra Hegadi, and G.G. Rajput

Fault Diagnosis for Industrial ImagesUsing a Min-Max Modular Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 842

Bin Huang and Bao-Liang Lu

Cellular Automata Based Pattern Classifying Machinefor Distributed Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 848

Pradipta Maji and P. Pal Chaudhuri

Investigating the Use of an Agent-Based Multi-classifier Systemfor Classification Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 854

Anne M. Canuto, Araken M. Santos, Marjory C. Abreu,Valeria M. Bezerra, Fernanda M. Souza, and Manuel F. Gomes Junior

A New MDS Algorithm for Textual Data Analysis . . . . . . . . . . . . . . . . . . . . . 860Manuel Martın-Merino and Alberto Munoz

XXIV Table of Contents

Neuroinformatics

Chaotic Behavior in Neural Networksand FitzHugh-Nagumo Neuronal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 868

Deepak Mishra, Abhishek Yadav, and Prem K. Kalra

Snap-Shotson Neuroinformatics and Neural Information Processing Research in Sin-gapore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 874

Lipo Wang

Deciphering the Genetic Blueprint of Cerebellar Developmentby the Gene Expression Profiling Informatics . . . . . . . . . . . . . . . . . . . . . . . . . . 880

Akira Sato, Noriyuki Morita, Tetsushi Sadakata, Fumio Yoshikawa,Yoko Shiraishi-Yamaguchi, JinHong Huang, Satoshi Shoji,Mineko Tomomura, Yumi Sato, Emiko Suga, Yukiko Sekine,Aiko Kitamura, Yasuyuki Shibata, and Teiichi Furuichi

Korean Neuroinformatics Research Program:From the Second Phase to the Third Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . 885

Soo-Young Lee

A Guided Tour of Neuroinformatics Research in India . . . . . . . . . . . . . . . . . . 891Prasun Kumar Roy and Nandini Chatterjee Singh

Fuzzy Systems

CMAC with Fuzzy Logic Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 898Daming Shi, Atul Harkisanka, and Chai Quek

A Fuzzy Multilevel Programming Methodfor Hierarchical Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 904

Bijay Baran Pal and Animesh Biswas

Fuzzy Rule-Based Systems Derived from Similarity to Prototypes . . . . . . . . 912W�lodzis�law Duch and Marcin Blachnik

Generalized Rule-Based Fuzzy Cognitive Maps:Structure and Dynamics Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 918

Vadim V. Borisov and Alexander S. Fedulov

Development of Adaptive Fuzzy Based Multi-user Detection Receiverfor DS-CDMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 923

Sharmistha Panda and Sarat Kumar Patra

A Partitioning Method for Fuzzy Probabilistic Predictors . . . . . . . . . . . . . . . 929Marcelo Andrade Teixeira and Gerson Zaverucha

Table of Contents XXV

Fuzzy Compactness Based Adaptive Window Approachfor Image Matching in Stereo Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 935

Gunjan and B.N. Chatterji

Neuro-fuzzy Systems

BDI Agents Using Neural Network and Adaptive Neuro Fuzzy Inferencefor Intelligent Planning in Container Terminals . . . . . . . . . . . . . . . . . . . . . . . . 941

Prasanna Lokuge and Damminda Alahakoon

A Neuro-fuzzy Approach for Predicting the Effects of Noise Pollutionon Human Work Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 947

Zaheeruddin and Garima

Evolving Fuzzy Neural Networks Applied to Odor Recognition . . . . . . . . . . . 953Cleber Zanchettin and Teresa B. Ludermir

Differential Evolution Based On-Line Feature Analysisin an Asymmetric Subsethood Product Fuzzy Neural Network . . . . . . . . . . . 959

C. Shunmuga Velayutham and Satish Kumar

Neuro-fuzzy System for Clustering of Video Database . . . . . . . . . . . . . . . . . . . 965Manish Manori A., Manish Maheshwari, Kuldeep Belawat,Sanjeev Jain, and P.K. Chande

Dynamic Neuro-fuzzy Inference and Statistical Models for Risk Analysisof Pest Insect Establishment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 971

Snjezana Soltic, Shaoning Pang, Nikola Kasabov, Sue Worner,and Lora Peackok

An Enhanced Fuzzy Multilayer Perceptron . . . . . . . . . . . . . . . . . . . . . . . . . . . . 977Kwang Baek Kim and Choong Shik Park

Hybrid Systems

Intelligent Multi-agent Based Genetic Fuzzy EnsembleNetwork Intrusion Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 983

Siva S. Sivatha Sindhu, P. Ramasubramanian, and A. Kannan

Genetic Algorithm Based Fuzzy ID3 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 989Jyh-Yeong Chang, Chien-Wen Cho, Su-Hwang Hsieh,and Shi-Tsung Chen

Neural-Evolutionary Learning in a Bounded Rationality Scenario . . . . . . . . 996Ricardo Matsumura de Araujo and Luıs C. Lamb

Rule Extraction Framework Using Rough Sets and Neural Networks . . . . . 1002Yi Xu and Narendra S. Chaudhari

XXVI Table of Contents

A Fusion Neural Network for Estimation of Blasting Vibration . . . . . . . . . . 1008A.K. Chakraborty, P. Guha, B. Chattopadhyay, S. Pal, and J. Das

Feature Analysis

Nonlinear Feature Extraction Using Evolutionary Algorithm . . . . . . . . . . . . 1014E.K. Tang, Ponnuthurai Nagaratnan Suganthan, and Xin Yao

Hybrid Feature Selection for Modeling Intrusion Detection Systems . . . . . 1020Srilatha Chebrolu, Ajith Abraham, and Johnson P. Thomas

Feature Selection for Fast Image Classificationwith Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1026

Zhi-Gang Fan, Kai-An Wang, and Bao-Liang Lu

Dimensionality Reduction by Semantic Mapping in Text Categorization . . 1032Renato Fernandes Correa and Teresa Bernarda Ludermir

Non-linear Dimensionality Reduction by Locally Linear Isomaps . . . . . . . . 1038Ashutosh Saxena, Abhinav Gupta, and Amitabha Mukerjee

Independent Component Analysis

Applications of Independent Component Analysis . . . . . . . . . . . . . . . . . . . . . 1044Erkki Oja

Supervised Independent Component Analysis with Class Information . . . . 1052Manabu Kotani, Hiroki Takabatake, and Seiichi Ozawa

Automated Diagnosis of Brain Tumours Using a Novel Density EstimationMethod for Image Segmentation and Independent Component AnalysisCombined with Support Vector Machines for Image Classification . . . . . . . 1058

Dimitris Glotsos, Panagiota Spyridonos, Panagiota Ravazoula,Dionisis Cavouras, and George Nikiforidis

Temporal Independent Component Analysisfor Separating Noisy Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1064

Liqing Zhang

Blind Dereverberation of Single-Channel Speech SignalsUsing an ICA-Based Generative Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1070

Jong-Hwan Lee, Sang-Hoon Oh, and Soo-Young Lee

Permutation Correction of Filter Bank ICAUsing Static Channel Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1076

Chandra Shekhar Dhir, Hyung Min Park, and Soo Young Lee

Table of Contents XXVII

Ant Colony

Minimal Addition-Subtraction Chains with Ant Colony . . . . . . . . . . . . . . . . 1082Nadia Nedjah and Luiza de Macedo Mourelle

TermitAnt: An Ant Clustering AlgorithmImproved by Ideas from Termite Colonies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1088

Vahid Sherafat, Leandro Nunes de Castro,and Eduardo R. Hruschka

Definition of Capacited p-Medians by a Modified Max Min Ant Systemwith Local Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1094

Fabrıcio Olivetti de Franca, Fernando J. Von Zuben,and Leandro Nunes de Castro

Investigations into the Use of Supervised Multi-agentsfor Web Documents Categorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1101

Siok Lan Ong, Weng Kin Lai, Tracy S.Y. Tai, Choo Hau Ooi,and Kok Meng Hoe

OrgSwarm – A Particle Swarm Model of Organizational Adaptation . . . . . 1110Anthony Brabazon, Arlindo Silva, Tiago Ferra de Sousa,Michael O’Neill, Robin Matthews, and Ernesto Costa

Neural Network Hardware

Analysis of Synchronous Time in Chaotic Pulse-Coupled Networks . . . . . . 1117Hidehiro Nakano and Toshimichi Saito

A Spiking Oscillator with Quantized Stateand Its Pulse Coding Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1123

Hiroshi Hamanaka, Hiroyuki Torikai, and Toshimichi Saito

Concurrent Support Vector Machine Processor for Disease Diagnosis . . . . 1129Jae Woo Wee and Chong Ho Lee

Robotics

Towards the Unification of Human Movement, Animation and Humanoidin the Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1135

Yasuo Matsuyama, Satoshi Yoshinaga, Hirofumi Okuda,Keisuke Fukumoto, Satoshi Nagatsuma, Kazuya Tanikawa,Hiroto Hakui, Ryusuke Okuhara, and Naoto Katsumata

A Dual Neural Network for Bi-criteria Torque Optimizationof Redundant Robot Manipulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1142

Shubao Liu and Jun Wang

XXVIII Table of Contents

A Genetic Approach to Optimizing the Values of Parametersin Reinforcement Learning for Navigation of a Mobile Robot . . . . . . . . . . . 1148

Keiji Kamei and Masumi Ishikawa

On the Use of Cognitive Artifacts for Developmental Learningin a Humanoid Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1154

Artur M. Arsenio

Visual Servo Control for Intelligent Guided Vehicle . . . . . . . . . . . . . . . . . . . . 1160J.K. Mukherjee

Signal Processing

A Basilar Membrane Model Using Simulink for Hearing-Aid Systems . . . . 1166Tetsuya Tsukada and Yoshifumi Sekine

Cluster and Intrinsic Dimensionality Analysisof the Modified Group Delay Feature for Speaker Classification . . . . . . . . . 1172

Rajesh M. Hegde and Hema A. Murthy

Two-Stage Duration Model for Indian LanguagesUsing Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1179

K. Sreenivasa Rao, S.R. Mahadeva Prasanna, and B. Yegnanarayana

Multichannel Blind Deconvolution of Non-minimum Phase SystemUsing Cascade Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1186

Bin Xia and Liqing Zhang

A Comparative Study of Feature Extraction Algorithmson ANN Based Speaker Model for Speaker Recognition Applications . . . . . 1192

Goutam Saha, Pankaj Kumar, and Sandipan Chakroborty

Development of FLANN Based Multireference Active Noise Controllersfor Nonlinear Acoustic Noise Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1198

Debi Prasad Das, Ganapati Panda, and Sanghamitra Sabat

Phase Space Parameters for Neural Network Based Vowel Recognition . . . 1204P. Prajith, N.S. Sreekanth, and N.K. Narayanan

Speaker Segmentation Based on Subsegmental Featuresand Neural Network Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1210

N. Dhananjaya, S. Guruprasad, and B. Yegnanarayana

Support Vector Machine

Morozov, Ivanov and Tikhonov Regularization Based LS-SVMs . . . . . . . . . 1216Kristiaan Pelckmans, Johan A.K. Suykens, and Bart De Moor

Table of Contents XXIX

A Study for Excluding Incorrect Detections of Holter ECG DataUsing SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1223

Yasushi Kikawa and Koji Oguri

Semi-supervised Kernel-Based Fuzzy C-Means . . . . . . . . . . . . . . . . . . . . . . . . 1229Daoqiang Zhang, Keren Tan, and Songcan Chen

Use of Autocorrelation Kernels in Kernel Canonical Correlation Analysisfor Texture Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1235

Yo Horikawa

Phoneme Transcription by a Support Vector Machine . . . . . . . . . . . . . . . . . . 1241Anurag Sahajpal, Terje Kristensen, and Gaurav Kumar

A Comparison of Pruning Algorithms for Sparse Least Squares SupportVector Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1247

L. Hoegaerts, J.A.K. Suykens, J. Vandewalle, and B. De Moor

Support Vector Machines Approach to Pattern Detectionin Bankruptcy Prediction and Its Contingency . . . . . . . . . . . . . . . . . . . . . . . . 1254

Kyung-shik Shin, Kyoung Jun Lee, and Hyun-jung Kim

Outliers Treatment in Support Vector Regressionfor Financial Time Series Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1260

Haiqin Yang, Kaizhu Huang, Laiwan Chan, Irwin King,and Michael R. Lyu

Kernel Based Clustering for Multiclass Data . . . . . . . . . . . . . . . . . . . . . . . . . . 1266D. Srikrishna Satish and C. Chandra Sekhar

Combined Kernel Function for Support Vector Machineand Learning Method Based on Evolutionary Algorithm . . . . . . . . . . . . . . . 1273

Ha-Nam Nguyen, Syng-Yup Ohn, and Woo-Jin Choi

Time Series Prediction

Neural Network Classification Algorithmfor the Small Size Training Set Situation in the Taskof Thin-Walled Constructions Fatigue Destruction Control . . . . . . . . . . . . . 1279

A.I. Galushkin, A.S. Katsin, S.V. Korobkova, and L.S. Kuravsky

Wavelet-Based Estimation of Hemodynamic Response Function . . . . . . . . . 1285R. Srikanth, R. Muralishankar, and A.G. Ramakrishnan

Neural Networks for fMRI Spatio-temporal Analysis . . . . . . . . . . . . . . . . . . . 1292Luo Huaien and Sadasivan Puthusserypady

XXX Table of Contents

Modeling Corrupted Time Series Datavia Nonsingleton Fuzzy Logic System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1298

Dongwon Kim, Sung-Hoe Huh, and Gwi-Tae Park

Hydrological Forecasting and Updating Procedures for Neural Network . . 1304Meuser Valenca and Teresa Ludermir

Bioinformatics

Modeling Gene Regulatory Network in Fission Yeast Cell CycleUsing Hybrid Petri Nets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1310

Ranjith Vasireddy and Somenath Biswas

Protein Metal Binding Residue Prediction Based on Neural Networks . . . . 1316Chin-Teng Lin, Ken-Li Lin, Chih-Hsien Yang, I-Fang Chung,Chuen-Der Huang, and Yuh-Shyong Yang

Assessment of Reliability of Microarray DataUsing Fuzzy C-Means Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1322

Musa Alci and Musa H. Asyali

DNA Sequence Pattern IdentificationUsing a Combination of Neuro-Fuzzy Predictors . . . . . . . . . . . . . . . . . . . . . . 1328

Horia-Nicolai Teodorescu and Lucian Iulian Fira

Genetic Mining of DNA Sequence Structures for Effective Classificationof the Risk Types of Human Papillomavirus (HPV) . . . . . . . . . . . . . . . . . . . 1334

Jae-Hong Eom, Seong-Bae Park, and Byoung-Tak Zhang

Gene Regulatory Network Discoveryfrom Time-Series Gene Expression Data –A Computational Intelligence Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1344

Nikola K. Kasabov, Zeke S.H. Chan, Vishal Jain, Igor Sidorov,and Dimiter S. Dimitrov

Sequence Variability and Long-Range Dependence in DNA:An Information Theoretic Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1354

Karmeshu and A. Krishnamachari

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1363