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Feedforward/Recurrent Fuzzy System Design Using Advanced Continuous Ant Colony Optimization
|關鍵字:||Continuous Ant Colony Optimization;連續螞蟻群聚最佳化;Feedforward;Recurrent;Fuzzy systems;前向型;遞迴型;模糊系統||出版社:||電機工程學系所||引用:|| X. Yao (Ed.), Evolutionary Computation—Theory and Applications, World Scientific, 1999.  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.  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.  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.  F. Hoffmann, D. Schauten, and S. Holemann, “Incremental evolutionary design of TSK fuzzy controllers,” IEEE Trans. Fuzzy Systems, vol. 15, no. 4, pp. 563-577, Aug. 2007.  E. G. Mansoori, M. J. Zolghadri, and S. D. 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This thesis proposes fuzzy system design using Rule-based Continuous Ant Colony Optimization (RCACO). RCACO determines the number of fuzzy rules and optimizes all the free parameters in each fuzzy rule. It uses an online rule-generation method to determine the number of rules and to identify suitable initial parameters for the rules, and then optimizes all free parameters using continuous ant colony optimization (ACO). In contrast to traditional ACO, which optimizes in the discrete domain, the RCACO optimizes parameters in the continuous domain and can achieve greater learning accuracy. In RCACO, the path of an ant is regarded as a combination of antecedent and consequent parameters from all rules. A new path selection method based on pheromone levels is proposed for initial solution construction. The solution is modified by sampling from a Gaussian probability density function, and it is then refined using the group best solution. Simulations on fuzzy control of three nonlinear plants are conducted to verify RCACO performance. Comparisons with other swarm intelligence and genetic algorithms demonstrate advantages of RCACO.
This thesis also proposes a recurrent fuzzy network design using Elite-guided Continuous Ant Colony Optimization (ECACO). The recurrent fuzzy network designed here is the Takagi-Sugeno-Kang (TSK)-type Recurrent Fuzzy Network (TRFN), in which each fuzzy rule contains feedback loops for handling dynamic system processing problems. ECACO optimizes all the free parameters in each recurrent fuzzy rule. In contrast to RCACO, the solution generated by continuous ACO is refined using the global elite solution with time-varying coefficients. To verify the performance of ECACO, controls of two dynamic plants and a continuous-stirred tank reactor are simulated. ECACO performance is also compared with other swarm intelligence and genetic algorithms in these simulations.
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