Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44341
標題: Designing Fuzzy-Rule-Based Systems Using Continuous Ant-Colony Optimization
作者: Juang, C.F.
莊家峰
Chang, P.H.
關鍵字: Ant-colony optimization (ACO)
fuzzy control
fuzzy-system (FS)
optimization
swarm intelligence (SI)
particle-swarm
genetic algorithm
controller-design
neural-network
interpretability
convergence
stability
space
ga
期刊/報告no:: Ieee Transactions on Fuzzy Systems, Volume 18, Issue 1, Page(s) 138-149.
摘要: This paper proposes the design of fuzzy-rule-based systems using 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 identify suitable initial parameters for the rules and then optimizes all the 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 the 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 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 the advantages of RCACO.
URI: http://hdl.handle.net/11455/44341
ISSN: 1063-6706
文章連結: http://dx.doi.org/10.1109/tfuzz.2009.2038150
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