Abstract
In this paper we analyze the neural network implementation of fuzzy logic proposed by Keller et al. [Fuzzy Sets Syst., 45, 1–12 (1992)], derive a learning algorithm for obtaining an optimal α for the net, and, for a special case, we show how one can directly (avoiding training) compute the optimal α. We address how training data can be generated for such a system. Effectiveness of the optimal α is then established through numerical examples. In this regard, several indices for performance evaluation are discussed. Finally, we propose a new architecture and demonstrate its effectiveness with numerical examples. © 1998 John Wiley & Sons, Inc.
