Reinforcement self-organizing interval type-2 fuzzy system with ant colony optimization

Authors:
Chia-Feng Juang;Chia-Hung Hsu;Chia-Feng Chuang
Affiliations:
Department of Electrical Engineering, National Chung-Hsing University, Taiwan, R.O.C.;Department of Electrical Engineering, National Chung-Hsing University, Taiwan, R.O.C.;Department of Electrical Engineering, National Chung-Hsing University, Taiwan, R.O.C.
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

Citing 11
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Abstract

This paper proposes a Reinforcement Self-Organizing Interval Type-2 Fuzzy System with Ant Colony Optimization (RSOIT2FS-ACO) method. The antecedent part in each fuzzy rule of the RSOIT2FS-ACO uses interval type-2 fuzzy sets in order to improve system robustness to noise. There are no fuzzy rules initially. The RSOIT2FS-ACO generates all rules online. The consequent part of each fuzzy rule is designed using Ant Colony Optimization (ACO). The ACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails. The RSOIT2FS-ACO method is applied to a truck backing control. The proposed RSOIT2FS-ACO is compared with other reinforcement fuzzy systems to verify its efficiency and effectiveness. A comparison with type-1 fuzzy systems verifies the robustness of using type-2 fuzzy systems to noise.