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Fuzzy Controller Design by Ant Colony Optimization with Fuzzy Clustering and Its FPGA Implementation
|引用:|| C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems: A Neural-Fuzzy Synergism to Intelligent Systems, Prentice Hall, May, 1996  L. X. Wang, Adaptive Fuzzy Systems and Control: Design and Stability Analysis, Prentice Hall, 1994.  T. B ck, Evolutionary Algorithms in Theory and Practice, New York: Oxford University Press, 1996.  X. Yao (editor), Evolutionary Computation - Theory and Applications, World Scientific, 1999.  O. Cordon, F. Herrera, F. Hoffmann and L. Magdalena, Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases, volume 19 of Advances in Fuzzy Systems - Applications and Theory, World Scientific, 2001.  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.  R. H. Li and Y. Zhang, “Fuzzy logic controller based on genetic algorithms,” Fuzzy Sets and Systems, vol. 83, pp. 1-10, 1996.  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.  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.  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.  C. C. Wong and S. M. Her, “A self-generating method for fuzzy system design,” Fuzzy Sets Syst., vol. 103, pp. 13-25, 1999.  C. F. Juang, “Temporal problems solved by dynamic fuzzy network based on genetic algorithm with variable-length chromosomes,” Fuzzy Sets Syst., vol. 142, no. 2, pp. 199-219, March 2004.  C. T. Lin and C. S. G. Lee, “Neural-network-based fuzzy logic control and decision system,” IEEE Trans. Comput., vol. 40, no. 12, 1991, pp. 1320-1336.  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.  J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York:Plenum Press, 1981.  M. Setnes, R. Babu ka, U. Kaymak, and H. R. N. Lemke, “Similarity measures in fuzzy rule base simplification,” IEEE Trans. Syst., Man, and Cyber.: Part B: Cybernetics, vol. 28, no. 3, pp. 376-386, June, 1998.  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.  U. Kaymak and M. Setnes, “Fuzzy clustering with volume prototypes and adaptive cluster merging,” IEEE Trans. Fuzzy Systems, vol. 10, no. 6, pp. 705-712, Dec. 2002.  M. Dorigo, V. Maniezzo, and A. Colorni, “Ant System: Optimization by a Colony of Cooperating Agents,” IEEE Trans. on Systems, man, and cybernetics, Part B: Cybernetics, vol. 26, no. 1, February 1996.  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, April 1997.  M. Dorigo, and G. Di Caro, “Ant Colony Optimization: A new meta-heuristic” Proc. of the Congress on Evolutionary Computation, vol.2, pp. 1470-1477, July 1999.  L. F. Escudero, “An inexact algorithm for the sequential ordering problem,” European Journal of Operations Research, pp. 232-253, 1988.  I. Watanabe, and S. Matsui, “Improving the performance of ACO algorithms by adaptive control of candidate set,” Proc. IEEE Int. Conf. IEEE Evolutionary Computation, vol. 2, pp. 1355-1362, 2003.  K. M. Sim, and W. H. Sun., “Ant colony optimization for routing and load-balancing: survey and new directions,” IEEE Trans. Systems, Man, and Cybernetics, Part A-Systems and Humans,, vol. 33, no. 5, pp. 560-572, Sept. 2003.  J. Cassillas, O.Cordon, I. F. Viana, “Learning cooperative linguistic rules using the best-worst ant system algorithm,” Int. Journal of Intelligent Systems, vol. 20, pp. 433-452, 2005.  M. Dorigo and T. St tzle, Ant Colony Optimization, MIT, July 2004.  R. S. Sutton and A. G. Barto, Reinforcement Learning, Chap. 6, The MIT Press, 1998.  J. Tanomaru and S. Omatu, “Process control by on-line trained neural controllers,” IEEE Trans. Ind. Electron., vol. 39, pp. 511-521, Dec. 1992.  C. J. Lin and C. T. Lin, “An ART-based fuzzy adaptive learning control network,” IEEE Trans. Fuzzy Systems, vol. 5, no. 4, pp. 477-496, Nov., 1997.  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.  S. Bade and B. Hutchings, “FPGA based stochastic neural network implementation,” Proc. Of IEEE Workshop on FPGAs for Custom Computing Machines, pp. 189-198, 1994.  T. H. S. Li, S. J. Chang, and Y. X. Chen, “Implementation of human-like driving skills by autonomous fuzzy behavior control on an FPGA-based car-like mobile robot,” IEEE Trans. Industrial Electronics, vol. 50, no. 5, pp. 867-880, Oct. 2003.  C.F. Juang, J.S. Chen, and H. J. Huang, “Temperature control by hardware implemented recurrent fuzzy controller,” Proc. Of IEEE Int. Conf. On Fuzzy Systems, Budapest, Hungary, July, 2004.  C. F. Juang and C. H. Hsu, “Temperature control by chip-implemented adaptive recurrent fuzzy controller designed by evolutionary algorithm,” IEEE Trans. Circuits and Systems- I: Regular Papers, vol. 25, no. 11, pp. 2376-2384, Nov. 2005.  C. Aporntewan and P. Chongstitvatana, “Hardware implementation of the compact genetic algorithm,” Prof. IEEE Congress on Evolutionary Computation, South Korean, pp. 624-629, 2001.  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.  P. Martin, “An analysis of random number generators for a hardware implementation of genetic programming using FPGAs and Handel-C,” Technical Report CSM-358, Dept. of Computer Science, University of Essex, pp. 1-13, Jan. 2004.|
This thesis proposes a novel design method of Fuzzy Controller by Ant Colony Optimization (ACO) algorithm with Fuzzy Clustering (FC-ACOFC). The objective of FC-ACOFC is to improve both the design efficiency of fuzzy controller and control performance. Structure of FC-ACOFC, including the number of rules and fuzzy sets in each input variable, is created on-line by a newly proposed fuzzy clustering method. In contrast to conventional grid-type partition, the antecedent part of FC-ACOFC is flexibly partitioned, and the phenomenon of highly overlapped fuzzy sets is avoided. Once a new rule is generated, the consequence is selected from a list of candidate control actions by ACO. In ACO, the tour of an ant is regarded as a combination of consequent actions selected from every rule. A pheromone matrix among all candidate consequent actions is constructed and an on-line learning algorithm for heuristic value update is proposed. Searching for the best one among all consequence combinations involves using the pheromone matrix and heuristic values. To verify the performance of FC-ACOFC, simulations on nonlinear system control, water bath temperature control and chaotic system control are performed. Simulations on these problems and comparisons with other algorithms have demonstrated the performance of FC-ACOFC. 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. Application of the ACO chip to fuzzy control a simulated water bath temperature control problem has verified the effectiveness of the designed chip.
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