Research and design of a fuzzy neural expert system

Abstract

We have developed a fuzzy neural expert system that has the precision and learning ability of a neural network. Knowledge is acquired from domain experts as fuzzy rules and membership functions. Then, they are converted into a neural network which implements fuzzy inference without rule matching. The neural network is applied to problem-solving and learns from the data obtained during operation to enhance the accuracy. The learning ability of the neural network makes it easy to modify the membership functions defined by domain experts. Also, by modifying the weights of neural networks adaptively, the problem of belief propagation in conventional expert systems can be solved easily. Converting the neural network back into fuzzy rules and membership functions helps explain the inner representation and operation of the neural network.