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Fuzzy System Design Using Cooperative Continuous Ant Colony Optimization
|關鍵字:||Continuous Ant Colony Optimization;連續螞蟻群聚最佳化;Cooperative;Fuzzy systems;合作型;模糊系統||出版社:||電機工程學系所||引用:|| M. Dorigo and T. St tzle, Ant Colony Optimization, MIT, July 2004.  C. F. Juang, “A hybrid of genetic algorithm and particle swarm optimization for recurrent network design,” IEEE Trans. Syst., Man, and Cyber., Part B: Cybernetics, vol. 34, no. 2, pp. 997-1006, April, 2004.  A. Chatterjee, K. Pulasinghe, K. Watanabe, and K. Izumi, “A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems,” IEEE Trans. Industrial Electronics, vol. 52, no. 6, pp. 1478-1489, Dec. 2005.  R. S. Parpinelli, H. S. Lopes, and A. A. Freitas, “Data mining with an ant colony optimization algorithm,” IEEE Trans. Evolutionary Computing, vol. 6, no. 4, pp. 321-332, Aug. 2002.  K. M. Sim and W. H. 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本論文提出以合作型連續螞蟻群聚 (CCACO)設計模糊系統。模糊系的設計於固定的模糊規則數目，藉由CCACO最佳化所有參數。比較原始的連續螞蟻的運用上，合作型連續螞蟻在解決最佳化的問題中能達到更好的學習效能。在CCACO裡，更新的參數包括前件部及後件部。原始的參數以費洛蒙作為基礎，對原始的解做高斯取樣，並且由全域最佳解重新定義獲得參數的更新。新解的獲得是根據規則的合作。對舊的模糊規則和新的模糊規則進行組合，獲得一個相應於獨立模糊規則的模糊系統。此模糊系統的評估結果即代表模糊規則的效能。本論文中以合作型連續螞蟻群聚 (CCACO)設計模糊系統，模擬了Type-I和Type-II的例子總共有七個。在Type-I和Type-II的結果上都有不錯的效能。結果證明在不同的例子也有好的效能。
This thesis proposes cooperative continuous ant colony optimization (CCACO) for fuzzy system design. The fuzzy system is designed with a fixed number of fuzzy rules and all parameters are optimized by the CCACO. The CCACO shows better performance than previous continuous ant colony optimization algorithms. The CCACO optimizes antecedent and consequent parameters. The original parameters are renewed through pheromone-based Gaussian sampling of the original solutions and attraction caused by a global best solution. New solutions are obtained based on cooperation of fuzzy rules. The old fuzzy rules and renewed fuzzy rules proceed combinations to obtain a corresponding independent fuzzy rule of fuzzy system. The performance of the rules in a fuzzy system is the evaluation results. This thesis applies CCACO to design type-1 and type-2 fuzzy systems in seven examples. The results show good performance in different examples.
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