Designing a self-adaptive union-based rule- antecedent fuzzy controller based on two step optimization

Authors:
Chang-Wook Han;Jung-Il Park
Affiliations:
School of Electrical Engineering and Computer Science, Yeungnam University, Gyongbuk, South Korea;School of Electrical Engineering and Computer Science, Yeungnam University, Gyongbuk, South Korea
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2006

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Abstract

A self-adaptive union-based rule-antecedent fuzzy controller (SURFCon), which can guarantee a parsimonious knowledge base with reduced number of rules, is proposed. The SURFCon 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 SURFCon, 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 SURFCon, while stochastic reinforcement learning refines the binary connections of GA-optimized SURFCon for further improvement of the performance index. A cart-pole system is considered to verify the effectiveness of the proposed method.