| 蚁群聚类算法的T-S模糊模型辨识 |
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| 引用本文： | 赵宝江.蚁群聚类算法的T-S模糊模型辨识[J].计算机工程与应用,2011,47(21):153-156. |
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| 作者姓名： | 赵宝江 |
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| 作者单位： | 牡丹江师范学院数学系,黑龙江牡丹江,157012 |
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| 基金项目： | 国家自然科学基金，黑龙江省普通高等学校青年学术骨干支持计划项目，牡丹江师范学院科学技术研究重点项目 |
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| 摘 要： | 基于T-S模型,提出一种非线性系统的模型辨识方法。利用蚁群聚类算法来进行结构辨识,确定系统的模糊空间和模糊规则数。在聚类的基础上,利用遗传算法辨识模糊模型的后件加权参数,得到一个精确的模糊模型,从而实现参数辨识。仿真结果验证了该方法的有效性,表明该方法能够实现非线性系统的辨识,辨识精度高,可当作复杂系统建模的一种有效手段。
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| 关 键 词： | 蚁群聚类算法 T-S模糊模型 系统辨识 |
| 修稿时间： | |
Identification of T-S fuzzy models based on ant colony clustering algorithm |
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| ZHAO Baojiang.Identification of T-S fuzzy models based on ant colony clustering algorithm[J].Computer Engineering and Applications,2011,47(21):153-156. |
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| Authors: | ZHAO Baojiang |
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| Affiliation: | ZHAO Baojiang Department of Mathematics,Mudanjiang Teachers College,Mudanjiang,Heilongjiang 157012,China |
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| Abstract: | A model identification approach of nonlinear systems is presented based on T-S model.To automatically acquire the fuzzy space structure of system and the number of fuzzy rules,the ant colony clustering algorithm is used in structure identification.Based on the cluster result,the parameters of conclusion of fuzzy model are identified by means of the genetic algorithm to obtain a precise fuzzy model and realize parameters identification.This proposed method realizes the identification of nonlinear system and improves greatly the precision of identification.The simulation results show the effectiveness of the proposed method. |
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| Keywords: | ant colony clustering algorithm T-S fuzzy model system identification
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