Please use this identifier to cite or link to this item:
|標題:||Ant Colony Optimization Incorporated With Fuzzy Q-Learning for Reinforcement Fuzzy Control||作者:||Juang, C.F.
|關鍵字:||Ant colony optimization (ACO);fuzzy control;fuzzy Q-learning;genetic;reinforcement learning;reinforcement learning;symbiotic evolution;design;system;identification||Project:||Ieee Transactions on Systems Man and Cybernetics Part a-Systems and Humans||期刊/報告no：:||Ieee Transactions on Systems Man and Cybernetics Part a-Systems and Humans, Volume 39, Issue 3, Page(s) 597-608.||摘要:||
This paper proposes the design of fuzzy controllers by ant colony optimization (ACO) incorporated with fuzzy-Q learning, called ACO-FQ, with reinforcements. For a fuzzy inference system, we partition the antecedent part a priori and then list all candidate consequent actions of the rules. In ACO-FQ, the tour of an ant is regarded as a combination of consequent actions selected from every rule. Searching for the best one among all combinations is partially based on pheromone trail. We assign to each candidate in the consequent part of the rule a corresponding Q-value. Update of the Q-value is based on fuzzy-Q learning. The best combination of consequent values of a fuzzy inference system is searched according to pheromone levels and Q-values. ACO-FQ is applied to three reinforcement fuzzy control problems: 1) water bath temperature control; 2) magnetic levitation control; and 3) truck backup control. Comparisons with other reinforcement fuzzy system design methods verify the performance of ACO-FQ.
|Appears in Collections:||電機工程學系所|
Show full item record
TAIR Related Article
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.