Foundations Of Fuzzy Systems
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Foundations of Fuzzy Systems
The strength of this book lies in its clear and precise examination of the theory of fuzzy systems. A rigorous study of the principles of fuzzy set theory supports the book's fundamental aim, which is to promote the development of fuzzy systems for successful real-world applications. The authors highlight two important application areas: approximate reasoning in knowledge-based systems, and fuzzy control. Reflecting the state of the art in fuzzy systems research, the book is both comprehensive and practical in its approach. Its illustration of key concepts is based on a detailed analysis of the underlying semantics. Each chapter is enhanced by useful historical notes and extensive references. The book presents several industrial case studies and exercises designed to increase its appeal to advanced students and researchers in computer science, applied mathematics and engineering.
Genetic Fuzzy Systems: Evolutionary Tuning And Learning Of Fuzzy Knowledge Bases
In recent years, a great number of publications have explored the use of genetic algorithms as a tool for designing fuzzy systems. Genetic Fuzzy Systems explores and discusses this symbiosis of evolutionary computation and fuzzy logic. The book summarizes and analyzes the novel field of genetic fuzzy systems, paying special attention to genetic algorithms that adapt and learn the knowledge base of a fuzzy-rule-based system. It introduces the general concepts, foundations and design principles of genetic fuzzy systems and covers the topic of genetic tuning of fuzzy systems. It also introduces the three fundamental approaches to genetic learning processes in fuzzy systems: the Michigan, Pittsburgh and Iterative-learning methods. Finally, it explores hybrid genetic fuzzy systems such as genetic fuzzy clustering or genetic neuro-fuzzy systems and describes a number of applications from different areas.Genetic Fuzzy System represents a comprehensive treatise on the design of the fuzzy-rule-based systems using genetic algorithms, both from a theoretical and a practical perspective. It is a valuable compendium for scientists and engineers concerned with research and applications in the domain of fuzzy systems and genetic algorithms.
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Combines the study of neural networks and fuzzy systems with symbolic artificial intelligence (AI) methods to build comprehensive AI systems. Describes major AI problems (pattern recognition, speech recognition, prediction, decision-making, game-playing) and provides illustrative examples. Includes applications in engineering, business and finance.