Evolving Connectionist Systems

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  • Edition: 2nd
  • Format: Paperback
  • Copyright: 2007-08-30
  • Publisher: Springer-Verlag New York Inc
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This second edition of Evolving Connectionist Systems presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems, as well as new trends including computational neuro-genetic modelling and quantum information processing related to evolving systems. New applications, such as autonomous robots, adaptive artificial life systems and adaptive decision support systems are also covered. The models and techniques used are connectionist-based and, where possible, existing connectionist models have been used and extended. Divided into four parts the book opens with evolving processes in nature; looks at methods and techniques that can be used in evolving connectionist systems; then covers various applications in bioinformatics and brain studies; finishing with applications for intelligent machines. Aimed at all those interested in developing adaptive models and systems to solve challenging real world problems in computer science and engineering.

Author Biography

Professor Nik Kasabov is the Founding Director and Chief Scientist of the Knowledge Engineering and Discovery Research Institute, Auckland, NZ. He holds a number of key positions, including Chair of the Adaptive Systems Task Force of the Neural Network Technical Committee of the IEEE. He has published extensively, and been Programme Chair of over 50 high-profile conferences.

Table of Contents

Foreword Ip. vii
Foreword IIp. ix
Prefacep. xi
Abstractp. xxi
Evolving Connectionist Methodsp. 1
Introductionp. 3
Everything Is Evolving, but What Are the Evolving Rules?p. 3
Evolving Intelligent Systems (EIS) and Evolving Connectionist Systems (ECOS)p. 8
Biological Inspirations for EIS and ECOSp. 11
About the Bookp. 13
Further Readingp. 13
Feature Selection, Model Creation, and Model Validationp. 15
Feature Selection and Feature Evaluationp. 15
Incremental Feature Selectionp. 20
Machine Learning Methods - A Classification Schemep. 21
Probability and Information Measure. Bayesian Classifiers, Hidden Markov Models. Multiple Linear Regressionp. 35
Support Vector Machines (SVM)p. 40
Inductive Versus Transductive Learning and Reasoning. Global, Local, and 'Personalised' Modellingp. 44
Model Validationp. 48
Exercisep. 49
Summary and Open Problemsp. 49
Further Readingp. 51
Evolving Connectionist Methods for Unsupervised Learningp. 53
Unsupervised Learning from Data. Distance Measurep. 53
Clusteringp. 57
Evolving Clustering Method (ECM)p. 61
Vector Quantisation. SOM and ESOMp. 68
Prototype Learning. ARTp. 73
Generic Applications of Unsupervised Learning Methodsp. 75
Exercisep. 81
Summary and Open Problemsp. 81
Further Readingp. 82
Evolving Connectionist Methods for Supervised Learningp. 83
Connectionist Supervised Learning Methodsp. 83
Simple Evolving Connectionist Methodsp. 91
Evolving Fuzzy Neural Networks (EFuNN)p. 97
Knowledge Manipulation in Evolving Fuzzy Neural Networks (EFuNNs) - Rule Insertion, Rule Extraction, Rule Aggregationp. 109
Exercisep. 124
Summary and Open Questionsp. 125
Further Readingp. 126
Brain Inspired Evolving Connectionist Modelsp. 127
State-Based ANNp. 127
Reinforcement Learningp. 132
Evolving Spiking Neural Networksp. 133
Summary and Open Questionsp. 139
Further Readingp. 140
Evolving Neuro-Fuzzy Inference Modelsp. 141
Knowledge-Based Neural Networksp. 141
Hybrid Neuro-Fuzzy Inference System (HyFIS)p. 146
Dynamic Evolving Neuro-Fuzzy Inference Systems (DENFIS)p. 149
Transductive Neuro-Fuzzy Inference Modelsp. 161
Other Evolving Fuzzy Rule-Based Connectionist Systemsp. 168
Exercisep. 175
Summary and Open Problemsp. 175
Further Readingp. 175
Population-Generation-Based Methods: Evolutionary Computationp. 177
A Brief Introduction to ECp. 177
Genetic Algorithms and Evolutionary Strategiesp. 179
Traditional Use of EC for Learning and Optimisation in ANNp. 183
EC for Parameter and Feature Optimisation of ECOSp. 185
EC for Feature and Model Parameter Optimisation of Transductive Personalised (Nearest Neighbour) Modelsp. 194
Particle Swarm Intelligencep. 198
Artificial Life Systems (ALife)p. 200
Exercisep. 201
Summary and Open Questionsp. 202
Further Readingp. 202
Evolving Integrated Multimodel Systemsp. 203
Evolving Multimodel Systemsp. 203
ECOS for Adaptive Incremental Data and Model Integrationp. 209
Integrating Kernel Functions and Regression Formulas in Knowledge-Based ANNp. 215
Ensemble Learning Methods for ECOSp. 219
Integrating ECOS and Evolving Ontologiesp. 225
Conclusion and Open Questionsp. 226
Further Readingp. 227
Evolving Intelligent Systemsp. 229
Adaptive Modelling and Knowledge Discovery in Bioinformaticsp. 231
Bioinformatics: Information Growth, and Emergence of Knowledgep. 231
DNA and RNA Sequence Data Analysis and Knowledge Discoveryp. 236
Gene Expression Data Analysis, Rule Extraction, and Disease Profilingp. 242
Clustering of Time-Course Gene Expression Datap. 259
Protein Structure Predictionp. 262
Gene Regulatory Networks and the System Biology Approachp. 265
Summary and Open Problemsp. 272
Further Readingp. 273
Dynamic Modelling of Brain Functions and Cognitive Processesp. 275
Evolving Structures and Functions in the Brain and Their Modellingp. 275
Auditory, Visual, and Olfactory Information Processing and Their Modellingp. 282
Adaptive Modelling of Brain States Based on EEG and fMRI Datap. 290
Computational Neuro-Genetic Modelling (CNGM)p. 295
Brain-Gene Ontologyp. 299
Summary and Open Problemsp. 301
Further Readingp. 302
Modelling the Emergence of Acoustic Segments in Spoken Languagesp. 303
Introduction to the Issues of Learning Spoken Languagesp. 303
The Dilemma 'Innateness Versus Learning' or 'Nature Versus Nurture' Revisitedp. 305
ECOS for Modelling the Emergence of Phones and Phonemesp. 307
Modelling Evolving Bilingual Systemsp. 316
Summary and Open Problemsp. 321
Further Readingp. 323
Evolving Intelligent Systems for Adaptive Speech Recognitionp. 325
Introduction to Adaptive Speech Recognitionp. 325
Speech Signal Analysis and Speech Feature Selectionp. 329
Adaptive Phoneme-Based Speech Recognitionp. 331
Adaptive Whole Word and Phrase Recognitionp. 334
Adaptive, Spoken Language Human-Computer Interfacesp. 338
Exercisep. 339
Summary and Open Problemsp. 339
Further Readingp. 340
Evolving Intelligent Systems for Adaptive Image Processingp. 341
Image Analysis and Feature Selectionp. 341
Online Colour Quantisationp. 344
Adaptive Image Classificationp. 348
Incremental Face Membership Authentication and Face Recognitionp. 350
Online Video-Camera Operation Recognitionp. 353
Exercisep. 357
Summary and Open Problemsp. 358
Further Readingp. 358
Evolving Intelligent Systems for Adaptive Multimodal Information Processingp. 361
Multimodal Information Processingp. 361
Adaptive, Integrated, Auditory and Visual Information Processingp. 362
Adaptive Person Identification Based on Integrated Auditory and Visual Informationp. 364
Person Verification Based on Auditory and Visual Informationp. 373
Summary and Open Problemsp. 379
Further Readingp. 380
Evolving Intelligent Systems for Robotics and Decision Supportp. 381
Adaptive Learning Robotsp. 381
Modelling of Evolving Financial and Socioeconomic Processesp. 382
Adaptive Environmental Risk of Event Evaluationp. 385
Summary and Open Questionsp. 390
Further Readingp. 391
What Is Next: Quantum Inspired Evolving Intelligent Systems?p. 393
Why Quantum Inspired EIS?p. 393
Quantum Information Processingp. 394
Quantum Inspired Evolutionary Optimisation Techniquesp. 396
Quantum Inspired Connectionist Systemsp. 398
Linking Quantum to Neuro-Genetic Information Processing: Is This The Challenge For the Future?p. 400
Summary and Open Questionsp. 402
Further Readingp. 403
A Sample Program in MATLAB for Time-Series Analysisp. 405
A Sample MATLAB Program to Record Speech and to Transform It into FFT Coefficients as Featuresp. 407
A Sample MATLAB Program for Image Analysis and Feature Extractionp. 411
Macroeconomic Data Used in Section 14.2 (Chapter 14)p. 415
Referencesp. 417
Extended Glossaryp. 439
Indexp. 453
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