| Self-learning Fuzzy Controllers Based On a Real-time Reinforcement Genetic Algorithm |
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| 作者姓名： | 方建安 苗清影 郭钊侠 邵世煌 |
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| 作者单位： | FANG Jian-an，MIAO Qing-ying，GUO Zhao-xia，SHAO Shi-huang College of Information Science & Technology，Dong Hua University，Shanghai，200051 |
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| 基金项目： | theYouthTeacherProgramFoundationoftheStateEducationDepartmentandtheShanghaiShuguangProgramFoudation |
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| 摘 要： | ＩｎｔｒｏｄｕｃｔｉｏｎＳｉｎｃｅｔｈｅｐｉｏｎｅｅｒｉｎｇｒｅｓｅａｒｃｈｏｆＭａｍｄａｎｉａｎｄｈｉｓｃｏｌｌｅａｇｕｅｓｏｎｆｕｚｚｙｃｏｎｔｒｏｌ1] ,ａｓａｐｏｗｅｒｆｕｌａｐｐｒｏａｃｈｔｏｃｏｎｔｒｏｌｓｙｓｔｅｍｓｗｉｔｈｅｘｐｅｒｉｅｎｃｅｋｎｏｗｌｅｄｇｅ,ｉｔｈａｓｂｅｅｎｗｉｄｅｌｙａｎｄｅｆｆｅｃｔｉｖｅｌｙａｐ ｐｌｉｅｄｔｏｔｈｅｃｏｎｔｒｏｌｏｒｍａｎｙｓｙｓｔｅｍｓ,ｉｎｃｌｕｄｉｎｇａｎｕｍｂｅｒｏｆｒｅａｌｗｏｒｌｄｐｈｙｓｉｃａｌｐｒｏｂｌｅｍｓ Ｔｈｅｓｅｓ…
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Self-learning Fuzzy Controllers Based On a Real-time Reinforcement Genetic Algorithm |
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| FANG Jian-an,MIAO Qing-ying,GUO Zhao-xia,SHAO Shi-huang.Self-learning Fuzzy Controllers Based On a Real-time Reinforcement Genetic Algorithm[J].Journal of Donghua University,2002,19(2). |
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| Authors: | FANG Jian-an MIAO Qing-ying GUO Zhao-xia SHAO Shi-huang |
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| Abstract: | This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result. |
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| Keywords: | fuzzy controller self-learning real time reinforcement genetic algorithm |
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