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Hierarchical Multi-Species PSO For Fuzzy System / Recurrent NN With Hexapod Robot Control Application
|關鍵字:||PSO;粒子群聚最佳化;Fuzzy System;Hexapod Robot;模糊系統;六足機器人||出版社:||電機工程學系所||引用:|| C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems: A Neural-Fuzzy Synergism to Intelligent Systems, Prentice Hall, May, 1996.  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, S. H. Chiu, and S. W. Chang, “A self-organizing TS-type fuzzy network with support vector learning and its application to classification problems,” IEEE Trans. Fuzzy Systems, vol. 15, no. 5, pp. 998-1008, Oct. 2007.  O. Cordoon, F. Herrera, F. Hoffmann and L. Magdalena, “Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases, ” Advances in Fuzzy Systems - Applications and Theory, vol. 19, World Scientific, 2001.  M. Russo, “Genetic fuzzy learning,” IEEE Trans. Evolutionary Computation, vol. 4, no. 3, pp. 259-273, 2000.  F. Hoffmann, D. Schauten, and S. 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This thesis proposes a Hierarchical Clustering-based Multi-Species Particle Swarm Optimization (HCMSPSO) algorithm, called HCMSPSO, for fuzzy system optimization. A swarm in HCMSPSO is clustered into multiple species in an upper hierarchical level, and each species is further clustered into multiple subspecies in a lower hierarchical level. For a fuzzy system consisting of rules, swarms (species) are formed in the upper level. One species only optimizes a single fuzzy rule, and the species cooperatively optimize a whole fuzzy system. There are initially no species in HCMSPSO. An on-line clustering-based algorithm is proposed to generate new species (fuzzy rules) automatically and determine proper initial particle positions for each species. Particles within different species represent different rules and are optimized independently in the lower level. For each species, subspecies are formed adaptively in each iteration according to clustering of particles. The best-performing particle in each subspecies serves as the neighborhood best when updating all particles within the same subspecies. Simulations on fuzzy control, modeling, and prediction problems are conducted to verify HCMSPSO performance. Comparisons with other advanced PSO optimization algorithms demonstrate HCMSPSO performance.
This thesis also proposes a Hierarchical Multi-Species Particle Swarm Optimization (HMSPSO) algorithm, called HMSPSO, for fully connected recurrent neural network (FCRNN) optimization. In HMSPSO, the number of species is equal to the number of nodes in an FCRNN and is assigned in advance. Each species only optimizes the free parameters in a node. Like HCMSPSO, subspecies are formed adaptively in each iteration according to clustering of particles in each species. Design of a single and multiple FCRNNs are conducted, where the latter network structure is applied to hexapod robot locomotion control problem. At first, a multiple-FCRNN controller is designed using a simulated hexapod robot. The designed controller is then successfully applied to a real hexapod robot control.
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