Implementation of fuzzy logic systems and neural networks in industry

Abstract

This paper presents details of the implementation of neural networks and/or fuzzy logic systems in industry, especially in the areas of scheduling and planning, inventory control, quality control, group technology and forecasting. The paper also covers the most current research in the fusion of neural networks and fuzzy logic systems. The four types of approach considered are (1) using neural networks to simulate membership functions in fuzzy logic systems; (2) using neural networks to replace fuzzy rule evaluation in fuzzy logic systems; (3) fusing neural networks logic systems; and (4) using neural networks to learn or process fuzzy types of data. However, because few industries have successfully implemented these approaches, detailed discussions are provided for stimulating future studies.