Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/7365
標題: 一個自我產生的模糊系統利用螞蟻和粒子族群群聚和其硬體實現
A Self-generating Fuzzy System with Ant and Particle Swarm Cooperative Optimization and Its FPGA Implementation
作者: 王啟彥
Wang, Chi-Yan
關鍵字: Swarm intelligence
族群智慧
ant colony optimization
particle swarm optimization
structure learning
fuzzy clustering
螞蟻族群最佳化
粒子族群佳化
架構學習
模糊分群
出版社: 電機工程學系所
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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. [8] M. Dorigo and T. St tzle, Ant Colony Optimization, MIT, July 2004. [9] R. Mendes, P. Cortez, M. Rocha, and J. Neves, “Particle swarms for feedforward neural network training,” Proc. of Int. Joint Conf. on Neural Networks, 2002, pp. 1895-1899. [10] S. L. Ho, S. Yang; G. Ni, and H. C .Wong, “A particle swarm optimization method with enhanced global search ability for design optimizations of electromagnetic devices,” IEEE Trans. Magnetics, vol. 42, no. 3, pp. 1107-1110, April 2006. [11] J. S. Heo, K. Y. Lee, and R. G. Ramirez, “Multiobjective control of power plants using particle swarm optimization techniques,” IEEE Trans. Energy Conversion, vol. 21, no. 2, pp. 552-561, June 2006. [12] R. S. Parpinelli, H. S. Lopes, and A. A. Freitas, “Data mining with an ant colony optimization algorithm,” IEEE Trans. Evolutionary Computing, vol. 6, no. 4, pp. 321-332, Aug. 2002. [13] K. M. Sim and W. H. Sun., “Ant colony optimization for routing and load-balancing: survey and new directions,” IEEE Trans. on Systems, Man, and Cybernetics, Part A: Systems and Humans, vol. 33, no. 5, pp. 560-572, Sept. 2003. [14] A. Homaifar and E. McCormick, “Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms,” IEEE Trans. Fuzzy Systems, vol. 3, no. 2, pp. 129-139, 1995. [15] I. F. Chung, C.J. Lin, and C.T. Lin, “A GA-based fuzzy adaptive learning control network,” Fuzzy Sets and Systems, vol. 112, pp. 65-84, 2000. [16] K. Belarbi and F. Titel, “Genetic algorithm for the design of a class of fuzzy controllers: an alternative approach,” IEEE Trans. Fuzzy Systems, vol. 8, no. 4, pp. 398-405, Aug., 2000. [17] C. F. Juang, J. Y. Lin, and C. T. Lin, “Genetic reinforcement learning through symbiotic evolution for fuzzy controller design, IEEE Trans. Systems, Man, and Cyber.-Part B. Cybernetics, vol. 30, no. 2, pp. 290-302, April, 2000. [18] C.F. Juang, “A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms,” IEEE Trans. Fuzzy Systems, vol.10, no. 2, pp. 155-170, April 2002. [19] C. F. Juang, “A hybrid of genetic algorithm and particle swarm optimization for recurrent network design,” IEEE Trans. Syst., Man, and Cyber., Part B: Cybernetics, Vol. 34, No. 2, pp. 997-1006, April, 2004. [20] J. Cassillas, O.Cordon, I. F. Viana, and F. Herrera, “Learning fuzzy rules using ant colony optimization algorithms, Prof. ANTS'2000 - From Ant Colonies to Artificial Ants: 2nd Int. Workshop on Ant Algorithms, pp. 12-21, Sep. 2000. [21] O. Cordon and F. Herrera, “A three-stage evolutionary process for learning descriptive and approximate fuzzy logic controller knowledge bases from examples,” Int. J. Approximate Reasoning, vol. 17, no. 4, pp. 369-407, 1997. [22] C. C. Wong and S. M. Her, “A self-generating method for fuzzy system design,” Fuzzy Sets Syst., vol. 103, pp. 13-25, 1999. [23] C. H. Chou, “Genetic algorithm-based optimal fuzzy controller design in the linguistic space,” IEEE Trans. Fuzzy Systems , vol. 14, no. 3, pp. 372-385, June 2006. [24] F. H ppner, F. Klawonn, R. Kruse, and T. Runkler, Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition, John Wiley & Sons, LTD, 1999. [25] C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems: A Neural-Fuzzy Synergism to Intelligent Systems, Prentice Hall, May, 1996. [26] 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. [27] B. Scheuermann , K. So, M. Guntsch, M. Middendorf, , O. Diessel, H. ElGindy, and H. Schmeck, “FPGA implementation of population-based ant colony optimization,” Applied Soft Computing, vol. 4, pp. 303-322, 2004. [28] P. 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摘要: 這篇論文提出了一個利用一個根據螞蟻和粒子族群群聚最佳化,線上自我對正分群(OSAC)的演算法所提出的學習能力,而能自我產生的模糊系統。而這個被提出的(OSAC)演算法不止幫助產生我們即時所訓練出的規則庫資料, 並且幫助我們避免產生高度重疊的模糊集合。當一個新的規則庫被產生時,和其相關的前件部和後件部參數由APSCO來做最佳化的動作。在APSCO裡,螞蟻族群和粒子族群共存在一個族群裡面,而且他們在每一次的疊代中,尋找一個同時的最佳參數解。螞蟻的路徑不止幫助去決定產生的規則庫的後件部參數,並且幫助產生一個輔助粒子族群。在輔助的粒子和原始的粒子裡,表現良好的粒子被選擇,而且經由粒子族群最佳化,被選擇的粒子合作找尋發現一個較好的解。而被提出的自我產生模糊系統,被應用在不同的模糊控制器設計問題中。和其它的演化和族群智慧演算法比較,而它們的結合被實施用來證實所提出方法的性能。 在此所採用的螞蟻群最佳化演算法的硬體實現是使用FPGA晶片。原因是近年來可程式邏輯(PLD)的使用越來越普遍,並且電路設計流程變快且具有彈性。
This paper proposes a self-generating fuzzy system with the learning ability coming from the proposal of an On-line Self-Aligning Clustering (OSAC) algorithm followed by Ant and Particle Swarm Cooperative Optimization (APSCO). The proposed OSAC algorithm not only helps to generate the rules from on-line training data but also helps to avoid the generation of highly overlapping fuzzy sets. Once a new rule is generated, the corresponding antecedent and consequent parameters are optimized by APSCO. In APSCO, ant colony and particle swarm coexist in a population, and they search an optimal parameter solution concurrently on each iteration. Ant paths not only help to determine the consequent parameters of generated rules but also help to generate auxiliary particles. Well-performed particles among the auxiliary particles and original particles are selected and the selected particles cooperate to find a better solution through particle swarm optimization. The proposed self-generating fuzzy system is applied to different fuzzy controller design problems. Comparisons with other evolutionary and swarm intelligence algorithms, and their hybrid are conducted to verify performance of the proposed method. The ACO used here is hardware implemented on Field Programmable Gate Array (FPGA) chip. The use of Programmable Logic Device (PLD) is more and more general in recent years, and the procedure of circuit deign is fast and elastic.
URI: http://hdl.handle.net/11455/7365
其他識別: U0005-0507200713231400
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0507200713231400
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