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Book Categories |
Foreword | ||
Acknowledgements | ||
Contributors | ||
1 | Intelligent Hybrid Systems: Issues, Classifications and Future Directions | 1 |
2 | Fuzzy Controller Synthesis with Neural Network Process Models | 23 |
3 | Replacing the Pattern Matcher of an Expert System with a Neural Network | 47 |
4 | Genetic Algorithms and Fuzzy Logic for Adaptive Process Control | 63 |
5 | Neural Network Weight Selection Using Genetic Algorithms | 85 |
6 | A Unified Approach For Engineering Design | 107 |
7 | A Hybrid System for Data Mining | 121 |
8 | Using Fuzzy Pre-processing with Neural Networks for Chemical Process Diagnostic Problems | 143 |
9 | A Multi-Agent Approach for the Integration of Neural Networks and Expert Systems | 153 |
10 | Integrating Symbol Processing Systems and Connectionist Networks | 177 |
11 | Reasoning with Rules and Variables in Neural Networks | 209 |
12 | The NeurOagent: A Neural Multi-agent Approach for Modelling, Distributed Processing and Learning | 221 |
13 | Genetic Programming of Neural Networks: Theory and Practice | 245 |
14 | Tools and Environments for Hybrid Systems | 275 |
Glossary | 309 | |
Intelligent Systems Resource Guide | 313 | |
Index | 321 |
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Add Intelligent Hybrid Systems, There is now a growing realisation in the intelligent systems community that many complex problems require hybrid solutions. Increasingly, hybrid systems combining genetic algorithms, fuzzy logic, neural networks and expert systems are proving their effec, Intelligent Hybrid Systems to the inventory that you are selling on WonderClubX
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Add Intelligent Hybrid Systems, There is now a growing realisation in the intelligent systems community that many complex problems require hybrid solutions. Increasingly, hybrid systems combining genetic algorithms, fuzzy logic, neural networks and expert systems are proving their effec, Intelligent Hybrid Systems to your collection on WonderClub |