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A Self-Organizing Interval Type-2 Fuzzy System With Reinforcement Ant Colony Learning
|關鍵字:||Ant colony optimization;螞蟻群聚最佳化;fuzzy Q-learning;type-2 fuzzy systems;fuzzy clustering;robot wall-following control.;模糊Q學習;第二類型模糊系統;模糊分群;機器人沿牆走控制||出版社:||電機工程學系所||引用:|| R. S. Sutton and A. G. Barto, Reinforcement Learning, The MIT Press, 1998.  H. R. Berenji and P. Khedkar, “Learning and tuning fuzzy logic controller through reinforcement,” IEEE Trans. Neural Networks, vol.3, pp. 724-740, May 1992.  C. T. Lin and C.S.G., “Reinforcement structure/parameter learning for neuro-network-based fuzzy logic control system,” IEEE Trans. Fuzzy Systems, vol. 2, pp. 46-63, Feb. 1994.  L. Jouffe, “Fuzzy inference system learning by reinforcement methods,” IEEE Trans. On Syst., Man and Cyber. - Part C: Applications and Reviews, vol. 28, no. 3, pp. 338-355, Aug. 1998.  M. J. Er and C. Deng, “Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning,” IEEE Trans. Systems, Man, and Cybernetics- Part B: Cybernetics, vol. 34, no. 3, pp. 1478-1489, June 2004.  K. Chiang, H. Y. 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This paper proposes a Self-Organizing Interval Type-2 Fuzzy System with Reinforcement ant colony learning (SOIT2FS-R) method and applies it to autonomous mobile robot control. The antecedent part in each fuzzy rule of the SOIT2FS-R uses interval type-2 fuzzy sets. There are no fuzzy rules initially. An online aligned interval type-2 fuzzy clustering (AIT2FC) method is proposed to generate fuzzy rules automatically. The AIT2FC not only flexibly partitions the input space, but also reduces the number of fuzzy sets in each input dimension, which improves the interpretability of designed fuzzy systems. The consequent part of each fuzzy rule is designed using Q-value aided Ant Colony Optimization (QACO). The QACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of whose values are updated using reinforcement signals. The SOIT2FS-R method is applied to two mobile robot-like control problems: truck backing control and mobile robot wall-following control. This study also conducts experiment in wall-following control for a practical mobile. The proposed SOIT2FS-R is compared with other reinforcement fuzzy systems to verify its efficiency and effectiveness. Comparisons with type-1 fuzzy systems verify the robustness of using type-2 fuzzy systems to noise and environment uncertainty.
|Appears in Collections:||電機工程學系所|
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