| 采用改进PSO的非线性系统T-S模糊模型辩识 |
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| 引用本文： | 肖健梅,王锡淮.采用改进PSO的非线性系统T-S模糊模型辩识[J].计算机工程与应用,2006,42(5):46-49. |
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| 作者姓名： | 肖健梅 王锡淮 |
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| 作者单位： | 上海海事大学电气自动化系,上海,200135 |
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| 基金项目： | 上海市教委资助项目;上海市重点学科建设项目 |
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| 摘 要： | 提出了一种新的T-S模糊模型的非线性系统辨识方法。采用自适应模糊C均值聚类算法确定模糊模型的前件结构及参数,用改进的粒子群优化(PSO)算法来辩识模糊模型的结论参数以获得系统参数的最优估计。仿真结果表明该方法是有效的。
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| 关 键 词： | T-S模糊模型 自适应模糊聚类 粒子群优化 系统辨识 |
| 文章编号： | 1002-8331-（2006）05-0046-04 |
| 收稿时间： | 2005-08 |
| 修稿时间： | 2005-08 |
T-S Fuzzy Model Identification for Nonlinear System Based on Improved PSO |
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| Xiao Jianmei,Wang Xihuai.T-S Fuzzy Model Identification for Nonlinear System Based on Improved PSO[J].Computer Engineering and Applications,2006,42(5):46-49. |
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| Authors: | Xiao Jianmei Wang Xihuai |
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| Affiliation: | Department of Electrical and Automation, Shanghai Maritime University, Shanghai 200135 |
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| Abstract: | A new identification algorithm of Takagi-Sugeno fuzzy model is proposed.An adaptive fuzzy clustering algorithm is applied to decide prefix construction and parameters of fussy model.A Particle Swarm Optimization(PSO) algorithm is used to get the result parameters of fussy model,which can gain optimal values of system parameters.The simulation results show that the method is effective. |
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| Keywords: | T-S fussy model adaptive fuzzy clustering particle swarm optimization system identification |
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