C++ Neural Networks and Fuzzy Logic: Preface

Neural−Trained Fuzzy Systems

So far we have considered how fuzzy logic plays a role in neural networks. The converse relationship, neural

networks in fuzzy systems, is also an active area of research. In order to build a fuzzy system, you must have a

set of membership rules for fuzzy categories. It is sometimes difficult to deduce these membership rules with

a given set of complex data. Why not use a neural network to define the fuzzy rules for you? A neural network

is good at discovering relationships and patterns in data and can be used to preprocess data in a fuzzy system.

Further, a neural network that can learn new relationships with new input data can be used to refine fuzzy

rules to create a fuzzy adaptive system. Neural trained fuzzy systems are being used in many commercial

applications, especially in Japan:

•  The Laboratory for International Fuzzy Engineering Research (LIFE) in Yokohama, Japan has a

backpropagation neural network that derives fuzzy rules and membership functions. The LIFE system

has been successfully applied to a foreign−exchange trade support system with approximately 5000

fuzzy rules.