Adaptive fuzzy systems and control: design and stability analysis

Fuzzy logic systems technology, particularly fuzzy control and fuzzy modeling techniques, is one of the most successful practical applications of fuzzy set and logic theory. In the past few years, the trend has been to combine fuzzy logic systems technology with artificial neural network technology to produce so called neural-fuzzy or fuzzy-neural systems. The objective is to take the advantages of both technologies to make adaptive intelligent machines capable of· learning from numerical training data generated by sensors as well as from linguistic rules from human experts. This 232-page monograph, with a foreword by Professor Lotfi A. Zadeh, is one of the first books on this combined technology and, in my opinion, is one of the best. The main purpose of the book is to show how to combine numerical and linguistic information into a common framework in a systematic and efficient way. The author clearly presents his theory with rigorous mathematical proofs, and the example applications are accompanied by computer simulations. The book contains 13 chapters, which are divided into two parts. In the first part (chaps. 1-7), a detailed description of fuzzy logic systems, especially those with product and fuzzy logic, is given, and four training algorithms are developed to learn output fuzzy sets and fuzzy control rules for the fuzzy logic systems. The resultant adaptive fuzzy logic systems are used to solve some control and signal processing problems in computer simulation, and the performance is compared to that of artificial neural networks. In the second part (chaps. 8-13), several adaptive fuzzy controllers and fuzzy identifiers are designed for nonlinear control and