- Covers fuzzy, neural, and hybrid fuzzy-neural approaches to control along with an extensive overview of standard control theory
- Carefully defines new mathematical concepts involved in fuzzy set theory and provides detailed examples of fuzzy control
- Introduces students to applications of neural networks in neural and neural-fuzzy control
- Provides the framework necessary for studying advanced fuzzy, neural and fuzzy-neural control architectures
- Includes exercises and suggested projects in each chapterA solutions manual is available with qualifying course adoptions
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.
