Efficiency validation of fuzzy domain theories using a neural network model

Abstract

Knowledge-Based Neural Network with Trapezoidal Fuzzy Set (KBNN/TFS) is a fuzzy neural network model, which handles trapezoidal fuzzy inputs with the abilities of fuzzy rule revision, verification and generation. Based on KBNN/TFS, an efficiency validation method is proposed to evaluate the rule inference complexity on KBNN/TFS. Besides, three methods that simplify the structure of this fuzzy rule-based neural network model are provided to enhance the inference efficiency. Fuzzy tabulation method, the first method, is performed to do rule combination by modeling the antecedents of some specific rules and then to eliminate the don't care variables in the rules. The second method, named transitive fuzzy rule compacting method, combines the rules with the transitive relations to decrease the computational load of inference. The third method, called identical antecedent unifying method, simplifies the redundant antecedents of rules by replacing the identical antecedents of the rules with a single specific antecedent. By these methods, the structure of rules can be simplified without changing the results of its inference. The proposed efficiency validation method is used to analyze and support the results of performing these three efficiency enhancing methods. Also the simulation results show that the efficiency is enhanced after performing these three efficiency enhancing methods.