Acquisition of fizzy Rules Using fizzy Neural Networks with Forgetting
Motohide Umano, Shiro Fukunaka, Itsuo Hatono, Hiroyuki Tamura · 1997
We acquire fuzzy rules from data using a fuzzy neural network. First, we generate an initial fuzzy neural network of the specified number of fuzzy rules that have the less number of good membership functions generated using a self-organization algorithm by T. Kohonen. Then, we tune and prune fuzzy rules based on a structural leaning algorithm with forgetting by M. Ishilcawa, where the numerals in the consequent part and the center values and widths of membership functions in the antecedent part are tuned and forgotten a little, and thus redundant rules and variables are pruned to acquire simpler, general rules. We apply the method to the iris classification problem by R.A. Fisher and have a very good result.