312 Pages 156 B/W Illustrations
    by Chapman & Hall

    Although the use of fuzzy control methods has grown nearly to the level of classical control, the true understanding of fuzzy control lags seriously behind. Moreover, most engineers are well versed in either traditional control or in fuzzy control-rarely both. Each has applications for which it is better suited, but without a good understanding of both, engineers cannot make a sound determination of which technique to use for a given situation.

    A First Course in Fuzzy and Neural Control is designed to build the foundation needed to make those decisions. It begins with an introduction to standard control theory, then makes a smooth transition to complex problems that require innovative fuzzy, neural, and fuzzy-neural techniques. For each method, the authors clearly answer the questions: What is this new control method? Why is it needed? How is it implemented? Real-world examples, exercises, and ideas for student projects reinforce the concepts presented.

    Developed from lecture notes for a highly successful course titled The Fundamentals of Soft Computing, the text is written in the same reader-friendly style as the authors' popular A First Course in Fuzzy Logic text. A First Course in Fuzzy and Neural Control requires only a basic background in mathematics and engineering and does not overwhelm students with unnecessary material but serves to motivate them toward more advanced studies.

    A PRELUDE TO CONTROL THEORY
    An Ancient Control System
    Examples of Control Problems
    Open-Loop Control Systems
    Closed-Loop Control Systems
    Stable and Unstable Systems
    A Look at Controller Design
    Exercises and Projects
    MATHEMATICAL MODELS IN CONTROL
    Introductory Examples: Pendulum Problems
    State Variables and Linear Systems
    Controllability and Observability
    Stability
    Controller Design
    State Variable Feedback Control
    Second-Order Systems
    Higher-Order Systems
    Proportional-Integral-Derivative Control
    Nonlinear Control Systems
    Linearization
    Exercises and Projects
    FUZZY LOGIC FOR CONTROL
    Fuzziness and Linguistic Rules
    Fuzzy Sets in Control
    Combining Fuzzy Sets
    Sensitivity of Functions
    Combining Fuzzy Rules
    Truth Tables for Fuzzy Logic
    Fuzzy Partitions
    Fuzzy Relations
    Defuzzification
    Level Curves and Alpha-Cuts
    Universal Approximation
    Exercises and Projects
    FUZZY CONTROL
    A Fuzzy Controller for an Inverted Pendulum
    Main Approaches to Fuzzy Control
    Stability of Fuzzy Control Systems
    Fuzzy Controller Design
    Exercises and Projects
    NEURAL NETWORKS FOR CONTROL
    What is a Neural Network? .
    Implementing Neural Networks
    Learning Capability
    The Delta Rule
    The Back Propagation Algorithm
    Example: Training a Neural Network
    Practical Issues in Training
    Exercises and Projects
    NEURAL CONTROL
    Why Neural Networks in Control
    Inverse Dynamics
    Neural Networks in Direct Neural Control
    Example: Temperature Control
    Neural Networks in Indirect Neural Control
    Exercises and Projects
    FUZZY-NEURAL AND NEURAL-FUZZY CONTROL
    Fuzzy Concepts in Neural Networks
    Basic Principles of Fuzzy-Neural Systems
    Basic Principles of Neural-Fuzzy Systems
    Generating Fuzzy Rules and Membership Functions
    Exercises and Projects
    APPLICATIONS
    A Survey of Industrial Applications
    Cooling Scheme for Laser Materials
    Color Quality Processing
    Identification of Trash in Cotton
    Integrated Pest Management Systems
    Comments
    Bibliography
    Index

    Biography

    Hung T. Nguyen, Nadipuram R. Prasad, Carol L. Walker, Elbert A. Walker

    "…Simple, concise, and easy to read from the student's perspective…a welcome addition to the…references in the fields of neural and fuzzy systems."
    SIAM Review Vol. 46, No. 1