Hybrid Intelligent Systems
Hybrid Intelligent Systems summarizes the strengths and weaknesses of five intelligent technologies: fuzzy logic, genetic algorithms, case-based reasoning, neural networks and expert systems, reviewing the status and significance of research into their integration. Engineering and scientific examples and case studies are used to illustrate principles and application development techniques. The reader will gain a clear idea of the current status of hybrid intelligent systems and discover how to choose and develop appropriate applications. The book is based on a thorough literature search of recent publications on research and development in hybrid intelligent systems; the resulting 50-page reference section of the book is invaluable.
The book starts with a summary of the five major intelligent technologies and of the issues in and current status of research into them. Each subsequent chapter presents a detailed discussion of a different combination of intelligent technologies, along with examples and case studies. Four chapters contain detailed case studies of working hybrid systems. The book enables the reader to:
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Research in Hybrid Intelligent Systems
Expert Systems and Neural Networks
The Use of Hybrid Systems in the Power Industry
12 other sections not shown
adaptive applications approach architecture artificial intelligence artificial neural networks backpropagation case-based reasoning case-based reasoning system certainty factors classification combination components Computational Intelligence condition monitoring Conference on Neural Congress on Computational connectionist connectionist expert systems control systems database decision diagnostic Engineering environment Evolutionary Computation example expert network fault fitness function fusion fuzzy control fuzzy control systems fuzzy expert systems fuzzy logic fuzzy rules fuzzy sets fuzzy systems genetic algorithms heuristic hexamine Hruska hybrid intelligent systems hybrid neural network hybrid systems IEEE International Conference IEEE World Congress implementation improve input integration intelligent technologies interface Kandel knowledge base knowledge-based system Kohonen Kuncicky Lacher layer learning Medsker membership functions models modules network and expert nodes operations optimal Orlando output parameters patterns performance problem Proceedings research and development rule base sensors solutions string subsymbolic symbolic systems and neural techniques Uhrig values variables vibration weights