Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44348
標題: Ant colony optimization algorithm for fuzzy controller design and its FPGA implementation
作者: Juang, C.F.
莊家峰
Lu, C.M.
Lo, C.
Wang, C.Y.
關鍵字: ant colony optimization (ACO)
field-programmable gate array (FPGA)
chip
fuzzy control
genetic algorithms (GAs)
swarm intelligence
temperature control
neural-network
hardware implementation
temperature control
genetic
algorithm
system
期刊/報告no:: Ieee Transactions on Industrial Electronics, Volume 55, Issue 3, Page(s) 1453-1462.
摘要: An ant colony optimization (ACO) application to a fuzzy controller (FC) design, called ACO-FC, is proposed in this paper for improving design efficiency and control performance, as well as ACO hardware, implementation. An FC's antecedent part, i.e., the "if" part of its composing fuzzy if-then rules, is partitioned in grid-type, and all candidate rule consequent values are then listed. An ant trip is regarded as a combination of consequent values selected from every rule. A pheromone matrix among all candidate consequent values is constructed. Searching for the best one among all combinations of rule consequent values is based mainly on the pheromone matrix. The proposed ACO-FC performance is shown to be better than other metaheuristic design methods on simulation examples. The ACO used in ACO-FC is based on the known ant colony system and is hardware implemented on a field-programmable gate array chip. The ACO chip application to fuzzy control of a simulated water bath temperature control problem has verified the designed chip effectiveness.
URI: http://hdl.handle.net/11455/44348
ISSN: 0278-0046
文章連結: http://dx.doi.org/10.1109/tie.2007.909762
Appears in Collections:電機工程學系所

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