ABSTRACT

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.
  • chapter 1|22 pages

    Foundation of Fuzzy Systems

    chapter 2|24 pages

    Determination of Membership Functions

    chapter 6|13 pages

    Basic Structure of Fuzzy Neural Networks

    chapter 12|22 pages

    The Basics of Factor Spaces

    chapter 15|12 pages

    Data Preprocessing