Fuzzy rule networks and its applications to decision-making under uncertainty

This paper proposes a new method for rule representation and rule inference, and based on the method proposes a new architecture of fuzzy neural networks, to be called fuzzy rule networks (FRN) in this paper. The paper also derives a Delta-learning rule for the FRN networks through mathematical derivation, and provides some criteria for rule combination in the FRN. By using these criteria we can reduce the number of rules, and therefore simplify the architecture of the FRN networks. A simulation is given to show that the learning algorithm and the criteria for rule combination developed in this paper is effective