Description of input-output relationships of nonlinear systems from data is one of many important knowledge acquisition problems. Artificial neural networks (NN) is an effective tool for the identification of such models. However, one disadvantage of NN modeling is that the knowledge acquired by NN is hard to extract. Fuzzy Neural Networks (FNN) can use the Back Propagation (BP) algorithm to identify and express input-output relationships in the form of fuzzy rules, thus leading to possible knowledge extraction by humans. In this talk, the speaker will present research on applications of FNN to modeling nonlinear systems. Example applications to be presented include modeling a steel making process, a weaving process, a decision making process, a human's subjective comfort level, a sake (rice wine) making process, etc.

Fuzzy modeling of nonlinear systems is a far reaching problem and FNN does not solve all problems. Other hybrid techniques researched by the speaker's group, such as hierarchical fuzzy modeling methods using FNN in combination with genetic algorithms (GA), will be presented.

The contents of this talk will be:

1. Introduction of fuzzy neural network(FNN)
2. Fuzzy modeling using FNN
o Basic Idea
o Actual Application (a steel making process)
3. Further study
o Hierarchical fuzzy modeling
o Combination of FNN and GA