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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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