Foundations of neural networks, fuzzy systems, and knowledge engineering - Info and Reading Options

"Foundations of neural networks, fuzzy systems, and knowledge engineering" Table Of Contents:

• 1- Foreword
• 2- Preface
• 3- The Faculty of Knowledge Engineering and Problem Solving
• 4- Introduction to AI paradigms
• 5- Heuristic problem solving; genetic algorithms
• 6- Why expert systems, fuzzy systems, neural networks, and hybrid systems for knowledge engineering and problem solving?
• 7- Generic and specific AI problems: Pattern recognition and classification
• 8- Speech and language processing
• 9- Prediction
• 10- Planning, monitoring, diagnosis, and control
• 11- Optimization, decision making, and games playing
• 12- A general approach to knowledge engineering
• 13- Problems and exercises
• 14- Conclusion
• 15- Suggested reading
• 16- Knowledge Engineering and Symbolic Artificial Intelligence
• 17- Data, information, and knowledge: Major issues in knowledge engineering
• 18- Data analysis, data representation, and data transformation
• 19- Information structures and knowledge representation
• 20- Methods for symbol manipulation and inference: Inference as matching; inference as a search
• 21- Propositional logic
• 22- Predicate logic: PROLOG
• 23- Production systems
• 24- Expert systems
• 25- Uncertainties in knowledge-based systems: Probabilistic methods
• 26- Nonprobabilistic methods for dealing with uncertainties
• 27- Machine-learning methods for knowledge engineering
• 28- Problems and exercises
• 29- Conclusion
• 30- Suggested reading
• 31- From Fuzzy Sets to Fuzzy Systems
• 32- Fuzzy sets and fuzzy operations
• 33- Fuzziness and probability; conceptualizing in fuzzy terms; the extension principle
• 34- Fuzzy relations and fuzzy implications; fuzzy propositions and fuzzy logic
• 35- Fuzzy rules, fuzzy inference methods, fuzzification and defuzzification
• 36- Fuzzy systems as universal approximators; Interpolation of fuzzy rules
• 37- Fuzzy information retrieval and fuzzy databases
• 38- Fuzzy expert systems
• 39- Pattern recognition and classification, fuzzy clustering, image and speech processing
• 40- Fuzzy systems for prediction
• 41- Control, monitoring, diagnosis, and planning
• 42- Optimization and decision making
• 43- Problems and exercises
• 44- Conclusion
• 45- Suggested reading
• 46- Neural Networks: Theoretical and Computational Models
• 47- Real and artificial neurons
• 48- Supervised learning in neural networks: Perceptrons and multilayer perceptrons
• 49- Radial basis functions, time-delay neural networks, recurrent networks
• 50- Neural network models for unsupervised learning: Adaptive resonance theory
• 51- Kohonen self-organizing topological maps
• 52- Neural networks as associative memories
• 53- On the variety of neural network models
• 54- Fuzzy neurons and fuzzy neural networks
• 55- Hierarchical and modular connectionist systems
• 56- Problems
• 57- Conclusion
• 58- Suggested reading
• 59- Neural Networks for Knowledge Engineering and Problem Solving
• 60- Neural networks as a problem-solving paradigm
• 61- Connectionist expert systems
• 62- Connectionist models for knowledge acquisition: One rule is worth a thousand data examples
• 63- Symbolic rules insertion in neural networks: Connectionist production systems
• 64- Connectionist systems for pattern recognition and classification; image processing
• 65- Connectionist systems for speech processing
• 66- Connectionist systems for prediction
• 67- Connectionist systems for monitoring, control, diagnosis, and planning
• 68- Connectionist systems for optimization and decision making
• 69- Connectionist systems for modeling strategic games
• 70- Problems
• 71- Conclusions
• 72- Suggested reading
• 73- Hybrid Symbolic, Fuzzy, and Connectionist Systems: Toward Comprehensive Artificial Intelligence
• 74- The hybrid systems paradigm
• 75- Hybrid connectionist production systems
• 76- Hybrid connectionist logic programming systems
• 77- Hybrid fuzzy connectionist production systems
• 78- ("Pure") connectionist production systems: The NPS architecture (optional)
• 79- Hybrid systems for speech and language processing
• 80- Hybrid systems for decision making
• 81- Problems
• 82- Conclusion
• 83- Suggested reading
• 84- Neural Networks, Fuzzy Systems, and Nonlinear Dynamical Systems: Chaos; Toward New Connectionist and Fuzzy Logic Models
• 85- Chaos
• 86- Fuzzy systems and chaos: New developments in fuzzy systems
• 87- Neural networks and chaos: New developments in neural networks
• 88- Problems
• 89- Conclusion
• 90- Suggested reading
• 91- Appendixes
• 92- References
• 93- Glossary
• 94- Index

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