| 一种新的TS模型辨识算法 |
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| 引用本文： | 林妹娇,陈水利. 一种新的TS模型辨识算法[J]. 厦门水产学院学报, 2013, 0(3): 219-224 |
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| 作者姓名： | 林妹娇 陈水利 |
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| 作者单位： | [1]福州大学数学与计算机科学学院,福建福州350108 [2]集美大学理学院,福建厦门361021 |
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| 基金项目： | 福建省科技厅产学研重大项目（2011H6020）; 福建省自然科学基金资助项目（2011J01013）; 厦门市科技计划项目（3502Z20123022） |
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| 摘 要： | 提出一种新的TS模型辨识算法.该算法思想：首先采用MCR算法（Mountain C-Regressionmethod）自动确定聚类数目和初始聚类中心,然后采用改进的GK（Gustafon-Kessl）聚类算法得到最优的划分矩阵,再根据最优划分矩阵计算系统前件参数的最优值,最后用自适应粒子群优化算法（Adaptive Parti-cle Swarm Optimization,APSO）对后件参数进行优化.此辨识算法能够用较少的规则数描述给定的未知系统,并且容易实现.仿真实验表明该算法能够实现非线性系统的辨识,并且可获得相对高的精度.
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| 关 键 词： | TS模型辨识 MCR算法 改进的GK聚类算法 自适应粒子群优化算法 |
A Novel TS Model Identification Algorithm |
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| LIN Mei-jiao,CHEN Shui-li. A Novel TS Model Identification Algorithm[J]. , 2013, 0(3): 219-224 |
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| Authors: | LIN Mei-jiao CHEN Shui-li |
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| Affiliation: | 1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China; 2. School of Science, Jimei University, Xiamen 361021, China) |
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| Abstract: | In this paper, a novel TS model identification algorithm is proposed. The identification algo-rithm is on the base of the following ideas： Firstly, the Mountain C-Regression method （MCR） is used to au- tomatically identify the number of clusters and initial cluster center. Secondly, the modified Gustafson - Kessl （GK） algorithm is used to obtain an optimal input - output space fuzzy partition matrix which provids the val-ues of premise parameters. Finally, Adaptive Particle Swarm Optimization （APSO） algorithm is adopted to precisely adjust consequent parameters. It can express a given unknown system with a small number of fuzzy rules and is easy to implement. The simulation results show the proposed algorithm realizes the identification of the nonlinear system with relative high accuracy. |
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| Keywords: | Takagi-Sugeno model identification Mountain C-Regression method MCR modified GK algorithm Adaptive Particle Swarm Optimization APSO |
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