A fuzzy rule-based neural network model for revising approximate domain knowledge
Hahn-Ming Lee, Bing-Hui Lu · 2002
In this paper, a knowledge-based fuzzy neural network model, named KBFNN, is proposed. The initial structure of KBFNN can be constructed by approximate fuzzy rules. These approximate fuzzy rules may be incorrect or incomplete. Then, the approximate fuzzy rules are revised by neural network learning. Also, the fuzzy rules can be extracted from a revised KBFNN. To construct KBFNN by fuzzy rules, two kinds of fuzzy neurons are proposed. They are S-neurons and G-neurons. Besides, the KBFNN is capable of fuzzy inference. For processing fuzzy number efficiently, the LR-type fuzzy numbers are used. In a sample example, a knowledge-based evaluator (KBE) is demonstrated. The experimental results are very encouraging.>