Applicability of Fuzzy Flip-Flops in the Implementation of Neural Networks

The concept of various type fuzzy flip-flops (F3) has already been proposed. We have done some investigations on a large scope of F3s based on different t-norms and conorms. Also we have shown that a few F3 types are suitable for realizing neurons in multilayer perceptrons. The aim of this paper is to present a comparison of the performance of several type neural networks based on fuzzy J-K and also fuzzy D flip-flops (the latter derived from the former type). The behavior of algebraic, Yager, Dombi and Hamacher type fuzzy flip-flop neural networks are presented. The best fitting t-norm and corresponding fuzzy flip-flop type will be presented in terms of function approximation capability.