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標題: 利用加強式螞蟻群聚學習之自我組織第二類型模糊系統
A Self-Organizing Interval Type-2 Fuzzy System With Reinforcement Ant Colony Learning
作者: 許嘉宏
Hsu, Chia-Hung
關鍵字: Ant colony optimization
fuzzy Q-learning
type-2 fuzzy systems
fuzzy clustering
robot wall-following control.
出版社: 電機工程學系所
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Computational Intelligence in Robotics and Automation, vol. 3, pp. 1264-1269, 2003. [18] J. M. Mendel, “Computing derivatives in interval type-2 fuzzy logic system,” IEEE Trans. On Fuzzy Systems, vol. 12. no. 1, pp. 84-98, Feb. 2004. [19] H. Hagras, “Comments on dynamical optimal training for interval type-2 fuzzy neural network (T2FNN),” IEEE Trans. Syst., Man and Cyber. - Part B: Cybernetics, vol. 36, no. 5, pp. 1206-1209, Oct. 2006. [20] G. M. Mendez and O. Castillo, “Interval type-2 TSK fuzzy logic systems using hybrid learning algorithm,” Proc. IEEE Int. Conf. Fuzzy Systems, pp. 230-235, May 22-25, 2005. [21] M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE Trans. On Syst., Man, and Cybe., Part B: Cybernetics, vol. 26, no. 1, pp. 29-41, Feb. 1996. [22] M. Dorigo and T. St tzle, Ant Colony Optimization, MIT, July 2004. [23] J. Cassillas, O.Cordon, I. F. Viana, and F. 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摘要: 這篇論文提出了一個利用加強式螞蟻群聚學習之自我組織第二類型模糊系統(SOIT2FS-R),並且應用在自主機器人控制上。SOIT2FS-R中每一條模糊規則前件部都是第二類型的模糊集合。一開始的模糊規則庫是空的。線上即時第二類型分群方法(AIT2FC)被提出來自動地產生模糊規則。AIT2FC不僅有靈活地分割輸入空間,也降低每一個輸入維度的模糊集合數量。每一條模糊規則後件部的設計使用Q值輔助螞蟻群最佳化(QACO),而QACO挑選後件部方法是利用候選行為集合相應的螞蟻費洛蒙痕跡和Q值。這兩個值利用加強訊號更新。SOIT2FS-R方法應用在兩種可移動機器人控制問題上:倒車入庫控制和移動機器人沿牆走控制。SOIT2FS-R和其他加強式模糊系統比較可驗證它的效能和功效。與第一類型模糊系統比較驗證了第二類型模糊系統對雜訊和不明確地環境的強健性。
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.
其他識別: U0005-1907200814441300
Appears in Collections:電機工程學系所



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