Fuzzy and Neural: Interactions and Applications Studies in Fuzziness and Soft Computing

The book can be broken down into two basic parts: (1) Chapter 3 through 6 the main ingredient is a neural net; and (2) Chapter 7 through 10 are about fuzzy neural nets. The chapters on (layered, feedforward) neural nets include using neural nets to approximate fuzzy systems, to equal fuzzy systems and to solve fuzzy problems.

The chapters on fuzzy neural nets focus on using them to approximate fuzzy systems or equal fuzzy systems. The topic of neural nets, fuzzy systems and fuzzy neural nets are brought together in Chapter 11 with the design of a fuzzy teaching machine whose inputs/outputs are verbal statements.
Fuzzy systems and neural networks are naturally complementary because they overlap in their usefulness for certain problems. Fuzzy and Neural: Interactions and Applications is for those who are interested in using these powerful tools together in a variety of ways, such as using neural nets to mimic fuzzy systems.

The first chapters introduce fuzzy mathematics and elements of layered, feed-forward neural nets. Later the authors examine fuzzy expert systems; fuzzy controllers; training and evaluation of neural networks for fuzzy problems; and "fuzzy teaching machines," which use verbal commands from human operators to guide performance.

With few references and fewer proofs, the book sacrifices some completeness for the sake of smooth reading, but much of that material can be found in more comprehensive texts. While this text doesn't require in-depth knowledge of fuzzy sets or neural nets, it certainly helps. A mathematical background extending to at least differential calculus is essential. --Rob Lightner