Fuzzy inference networks: an introduction

One of the problems of existing fuzzy-neural approaches is that the logic nature of the structure is often lost, i.e., what is being processed by the neural networks becomes irrelevant. To retain this logic content while benefiting from the advantage of integrating fuzzy set and neural network approaches, we propose in this paper a fuzzy neural network which supports fuzzy inference mechanisms by being based exclusively on logic implication neurons. A supervised learning method involving an equality performance measure and an online update delta rule (gradient-based) learning procedure is used. An experimental study involving Wolfer's sunspot numbers is carried out, demonstrating fast convergence accompanied by explicit format of the inference network.