Evolutionary Optimization of Union-Based Rule-Antecedent Fuzzy Neural Networks and Its Applications
A union-based rule-antecedent fuzzy neural networks (URFNN), which can guarantee a parsimonious knowledge base with reduced number of rules, is proposed. The URFNN allows union operation of input fuzzy sets in the antecedents to cover bigger input domain compared with the complete structure rule which consists of AND combination of all input variables in its premise. To construct the URFNN, we consider the union-based logic processor (ULP) which consists of OR and AND fuzzy neurons. The fuzzy neurons exhibit learning abilities as they come with a collection of adjustable connection weights. In the development stage, genetic algorithm (GA) constructs a Boolean skeleton of URFNN, while gradient-based learning refines the binary connections of GA-optimized URFNN for further improvement of the performance index. A cart-pole system is considered to verify the effectiveness of the proposed method.
KeywordsGenetic Algorithm Rule Base Evolutionary Optimization Structure Rule Union Operation
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