Rp. Li et M. Mukaidono, A NEW APPROACH TO RULE LEARNING BASED ON FUSION OF FUZZY-LOGIC AND NEURAL NETWORKS, IEICE transactions on information and systems, E78D(11), 1995, pp. 1509-1514
A new method is developed to generate fuzzy rules from numerical data.
This method consists of two algorithms: Algorithm 1 is used to identi
fy structures of the given data set, that is, the optimal number of ru
les of system; Algorithm 2 is used to identify parameter of the used m
odel. The former is belonged to unsupervised learning, and the latter
is belonged to supervised learning. To identify parameters of fuzzy mo
del, we developed a neural network which is referred to as Unsymmetric
al Gaussian Function Network (UGFN). Unlike traditional fuzzy modellin
g methods, in the present method, a) the optimal number of rules (clus
ters) is determined by input-output data pairs rather than by only out
put data as in sugeno's method, b) parameter identification of the pre
sent model is based on a like-RBF network rather than backpropagation
algorithm. Our method is simple and effective because it integrates fu
zzy logic with neural networks from basic network principles to neural
architecture, thereby establishing an unifying framework for differen
t fuzzy modelling methods such as one with cluster analysis or neural
networks and so on.