Evolving Connectionist Systems: The Knowledge Engineering Approach

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Springer Science & Business Media, Aug 23, 2007 - Computers - 451 pages
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This second edition of the must-read work in the field presents generic computational models and techniques that can be used for the development of evolving, adaptive modeling systems, as well as new trends including computational neuro-genetic modeling 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.

 

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Contents

Abstract xxi
1
Feature Selection Model Creation and Model Validation
15
Evolving Connectionist Methods for Unsupervised Learning
53
Evolving Connectionist Methods for Supervised Learning
83
Brain Inspired Evolving Connectionist Models
127
Evolving NeuroFuzzy Inference Models
141
Evolutionary Computation
177
Evolving Integrated Multimodel Systems
203
Dynamic Modelling of Brain Functions and Cognitive Processes
275
Modelling the Emergence of Acoustic Segments in Spoken Languages
303
Evolving Intelligent Systems for Adaptive Speech Recognition
325
Information
373
Appendix A A Sample Program in MATLAB for TimeSeries Analysis
405
Extended Glossary
439
Index
453
Copyright

Evolving Intelligent Systems 229
230

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About the author (2007)

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.