Fuzzy Neural Intelligent Systems: Mathematical Foundation and the Applications in Engineering
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Overview
Although fuzzy systems and neural networks are central to the field of soft computing, most research work has focused on the development of the theories, algorithms, and designs of systems for specific applications. There has been little theoretical support for fuzzy neural systems, especially their mathematical foundations.
Fuzzy Neural Intelligent Systems fills this gap. It develops a mathematical basis for fuzzy neural networks, offers a better way of combining fuzzy logic systems with neural networks, and explores some of their engineering applications. Dividing their focus into three main areas of interest, the authors give a systematic, comprehensive treatment of the relevant concepts and modern practical applications:
- Fundamental concepts and theories for fuzzy systems and neural networks.
- Foundation for fuzzy neural networks and important related topics
- Case examples for neuro-fuzzy systems, fuzzy systems, neural network systems, and fuzzy-neural systems
Suitable for self-study, as a reference, and ideal as a textbook, Fuzzy Neural Intelligent Systems is accessible to students with a basic background in linear algebra and engineering mathematics. Mastering the material in this textbook will prepare students to better understand, design, and implement fuzzy neural systems, develop new applications, and further advance the field.
Synopsis
Although fuzzy systems and neural networks are central to the field of soft computing, most research work has focused on the development of the theories, algorithms, and designs of systems for specific applications. There has been little theoretical support for fuzzy neural systems, especially their mathematical foundations.
Fuzzy Neural Intelligent Systems fills this gap. It develops a mathematical basis for fuzzy neural networks, offers a better way of combining fuzzy logic systems with neural networks, and explores some of their engineering applications. Dividing their focus into three main areas of interest, the authors give a systematic, comprehensive treatment of the relevant concepts and modern practical applications:
Suitable for self-study, as a reference, and ideal as a textbook, Fuzzy Neural Intelligent Systems is accessible to students with a basic background in linear algebra and engineering mathematics. Mastering the material in this textbook will prepare students to better understand, design, and implement fuzzy neural systems, develop new applications, and further advance the field.
Booknews
Authors, Li (Beijing Normal U.), G.L. Philip Chen (Wright State U., Dayton), and Han-Pang Huang (National Taiwan U.), present the mathematical foundation for fuzzy neural networks and a format for combining neural networks with fuzzy logic systems. The intended audience includes engineers, scientists and researchers, as well as graduate students who have had linear algebra and engineering mathematics. The final five (of 19) chapters apply the mathematical foundation to extensive case examples for neuro-fuzzy systems, fuzzy systems, neural network systems, and fuzzy-neural systems. Annotation c. Book News, Inc., Portland, OR (booknews.com)