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Dynamic, genetic, and chaotic programming Book

Dynamic, genetic, and chaotic programming
Dynamic, genetic, and chaotic programming, Natural language, decision making, nonstationary environments, and other dynamic processes and systems represent some of the greatest challenges facing sixth generation computer technologies. Here, for the first time, is a practical software engineering a, Dynamic, genetic, and chaotic programming has a rating of 3.5 stars
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Dynamic, genetic, and chaotic programming, Natural language, decision making, nonstationary environments, and other dynamic processes and systems represent some of the greatest challenges facing sixth generation computer technologies. Here, for the first time, is a practical software engineering a, Dynamic, genetic, and chaotic programming
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  • Dynamic, genetic, and chaotic programming
  • Written by author Branko Soucek,The IRIS Group
  • Published by New York : Wiley, c1992., 1992/06/24
  • Natural language, decision making, nonstationary environments, and other dynamic processes and systems represent some of the greatest challenges facing sixth generation computer technologies. Here, for the first time, is a practical software engineering a
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Preface
Acknowledgments
Pt. I Dynamic Neural Networks Versus Automated Knowledge Acquisition
1 Paradigm/Problem Matching 3
2 Dynamic Systems Control via Associative Reinforcement Learning 27
3 Knowledge Acquisition for Dynamic System Control 65
4 Analysis of Recurrent Back Propagation and Its Application to Language Acquisition 85
5 Recognition and Restoration of Periodic Patterns with Time-dependent Recurrent Neural Network 107
6 Adaptive Stack Filtering by LMS and Perception Learning 119
7 Automatic Modeling of Acoustic Emission Phenomena by Neural Networks 145
8 Managing the Traffic of a Satellite Communication Network by Neural Network 165
Pt. II Genetic Algorithm and Genetic Programming
9 An Introduction to Adaptive Optimization AIgorithms Based on Principles of Natural Evolution 183
10 The Genetic Programming Paradigm: Genetically Breading [sic] Populations of Computer Programs to Solve Problems 203
11 Genetic Algorithms in Robotics 323
12 Efficient Multiprocessor Scheduling Based on Genetic Algorithms 339
13 A Genetic Algorithm for Point Pattern Matching 353
14 Artificial Embryology: The Genetic Programming of an Artificial Embryo 373
15 Structure Evolution in Neural Systems 395
16 Evolution Strategies for Circuit Partitioning 413
Pt. III The Software of Chaos With Applications
17 Continuous Chaos and Logical Chaos 439
18 Prediction of Chaotic Dynamical Phenomena by a Neural Network 471
19 Dynamic versus Genetic versus Chaotic Programming 501
20 Parallel Scheduling of Random and of Chaotic Processes 535
Index 541


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