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Chapman & Hall/CRC
12 November 2002
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.
By:   Hung T. Nguyen (New Mexico State University Las Cruces USA), Nadipuram R. Prasad (New Mexico State University, Las Cruces, USA), Carol L. Walker (New Mexico State University, Las Cruces, USA), Elbert A. Walker (New Mexico State University, Las Cruces, USA)
Imprint:   Chapman & Hall/CRC
Country of Publication:   United States
Dimensions:   Height: 235mm,  Width: 156mm,  Spine: 22mm
Weight:   680g
ISBN:   9781584882442
ISBN 10:   1584882441
Pages:   312
Publication Date:   12 November 2002
Audience:   College/higher education ,  A / AS level
Format:   Hardback
Publisher's Status:   Active
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

Reviews for A First Course in Fuzzy and Neural Control

Simple, concise, and easy to read from the student's perspectivea welcome addition to thereferences in the fields of neural and fuzzy systems.

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