Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8138
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
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: [1] R. S. Sutton and A. G. Barto, Reinforcement Learning, The MIT Press, 1998. [2] 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. [3] 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. [4] 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. [5] 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. [6] K. Chiang, H. Y. Chung, and J. J. Lin, “A self-learning fuzzy logic controller using genetic algorithms with reinforcements,” IEEE Trans. Fuzzy Systems, vol. 5, no. 3, pp. 460-467, 1997. [7] C.F. Juang, J.Y. Lin and C.T. Lin, “Genetic reinforcement learning through symbiotic evolution for fuzzy controller design,” IEEE Trans. Syst., Man, Cybern., Part B: Cybernetics, vol. 30, no. 2, pp. 290-302, April 2000. [8] C. J. Lin and Y. J. Xu, ” Efficient reinforcement learning through dynamical symbiotic evolution for TSK-type fuzzy controller design,” International Journal General Systems, vol. 34, no.5, pp. 559-578, Oct. 2005. [9] C. F. Juang, “Combination of on-line clustering and Q-value based GA for reinforcement fuzzy system design,” IEEE Trans. Fuzzy Systems, vol. 13, no. 3, pp. 289-302, June 2005. [10] N. N. Karnik , J. M. Mendel, and Q. Liang, “Type-2 fuzzy logic systems,” IEEE Trans. On Fuzzy Systems, vol. 7, no. 6, pp. 643-658, 1999. [11] J. M. Mendel, Uncertain Rule-Based Fuzzy Logic System: Introduction and New Directions, Prentice Hall, Upper Saddle River, NJ2001. [12] Q. Liang and J. M. Mendel, “Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters,” IEEE Trans. Fuzzy systems, vol. 8, no. 551-563, 2000. [13] J. Zeng and Z. Q. Liu, “Type-2 fuzzy hidden Markov models and their application to speech recognition,” IEEE Trans. Fuzzy Systems, vol. 14, no. 3, pp. 454-466, June 2006. [14] H. Hagras, “A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots,” IEEE Trans. Fuzzy Systems, vol. 12, no. 524-539, 2004. [15] C. Hwang and F. C. H. Rhee, “Uncertain fuzzy clustering: interval type-2 fuzzy approach to C-means,” IEEE Trans. Fuzzy Systems, vol. 15. no. 1, pp. 107-120, Feb. 2007. [16] Q. Liang and J. M. Mendel, “Interval type-2 fuzzy logic systems: theory and design,” IEEE Trans. On Fuzzy Systems, vol. 8, no. 5, pp. 535-550, 2000. [17] C. H. Lee, Y. C. Lin, and W. Y. Lai, “Systems identification using type-2 fuzzy neural network (Type-2 FNN) systems,” Proc. IEEE Int. Symp. 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. Herrera, “Learning cooperative linguistic rules using the best-worst ant system algorithm,” Int. Journal of Intelligent Systems, vol. 20, pp. 433-452, 2005. [24] M. Dorigo and L.M. Gambardella, “Ant colony system: A cooperative learning approach to the traveling salesman problem,” IEEE Trans. On Evolutionary Computation, vol. 1, no. 1, pp. 53-66, April 1997. [25] C. Blum and M. Dorigo, “The hyper-cube framework for ant colony optimization,” IEEE Trans. Syst., Man, and Cyber.-Part B: Cybernetics, vol. 34, no. 2, pp. 1161-1172, April 2004. [26] C. J. Wakins and P. Dayan, “Q-learning,” Machine Learning, vol. 8, no. 3, pp. 279-292, 1992. [27] C.F. Juang and C.T. Lin, “An on-line self-constructing neural fuzzy inference network and its applications,” IEEE Trans. Fuzzy Systems vol.6 pp. 12-32 Feb. 1998. [28] D. Wu and J. M. Mendel, “A vector similarity measure for interval type-2 fuzzy sets,” Porc. IEEE Int. Conf. Fuzzy Systems, pp. 1-6, July 2007. [29] P. Y. Glorennec and L. Jouffe, “Fuzzy Q-learning,” Proc. Of IEEE Int. Conf. On Fuzzy Systems, pp. 659-662, 1997. [30] S. G. Kong and B. Kosko, “Comparison of fuzzy and neural truck backer upper control systems,” Proc. Of Int. Joint. Conf. On Neural Networks, vol. 3, pp. 349-358, June 1990. [31] L. X. Wang and J. M. Mendel, “Generating fuzzy rules by learning from examples,” IEEE Trans. Syst., Man, and Cyber., vol. 22, no. 6, pp. 1414-1427, 1992. [32] C. F. Juang and C. I. Lee, “A fuzzified neural fuzzy inference network for handling both linguistic and numerical information simultaneously,” Neurocomputing, vol. 71, no. 1-3, pp. 342-352, Dec. 2007. [33] MobileRobots Inc. http://www.mobilerobots.com/
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.
URI: http://hdl.handle.net/11455/8138
其他識別: U0005-1907200814441300
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1907200814441300
Appears in Collections:電機工程學系所

文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.