[1]刘楠,刘福才,孟爱文.基于改进PSO和FCM的模糊辨识[J].智能系统学报,2019,14(02):378-384.[doi:10.11992/tis.201707025]
 LIU Nan,LIU Fucai,MENG Aiwen.Fuzzy identification based on improved PSO and FCM[J].CAAI Transactions on Intelligent Systems,2019,14(02):378-384.[doi:10.11992/tis.201707025]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年02期
页码:
378-384
栏目:
出版日期:
2019-03-05

文章信息/Info

Title:
Fuzzy identification based on improved PSO and FCM
作者:
刘楠 刘福才 孟爱文
燕山大学 电气工程学院, 河北 秦皇岛 066004
Author(s):
LIU Nan LIU Fucai MENG Aiwen
College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
关键词:
模糊辨识非线性系统模糊C均值聚类算法T-S模型智能算法粒子群算法Box-Jenkins数据辨识全局优化
Keywords:
fuzzy identificationnonlinear systemfuzzy C-meansT-S modelintelligent algorithmparticle swarm optimizationBox-Jenkins identificationglobal optimization
分类号:
TP15
DOI:
10.11992/tis.201707025
摘要:
为了提高T-S模糊模型的辨识精度和效率,本文提出了一种改进的粒子群算法和模糊C均值聚类算法相结合的模糊辨识新方法。在该方法中,针对粒子群算法在处理高维复杂函数时容易陷入局部极值的问题,提出了一种粒子群局部搜索和全局搜索动态调整的全新优化算法。模糊C均值聚类算法是模糊辨识最常用的方法之一,该算法简单,计算效率高,但是对初始化特别敏感,容易陷入局部最优。为了解决这一问题,利用改进粒子群算法的全局搜索能力优化聚类中心,显著地提高了算法的辨识精度和效率。最后,针对非线性系统进行建模仿真,仿真结果表明了本文方法的有效性和优越性。
Abstract:
To improve the accuracy and efficiency of T-S model, a new fuzzy identification approach based on improved particle swarm optimization (PSO) algorithm and fuzzy C-means (FCM) algorithm is proposed. Considering that it is easy for PSO to fall into the local extremum in the treatment of high-dimensional complex functions, a PSO algorithm with dynamic adjustment between local search and global search is proposed in this paper. Moreover, the FCM algorithm is one of the most commonly used methods of fuzzy identification. The algorithm is simple and efficient, but it is particularly sensitive to initialization and easily falls into local optimum. To solve this problem, the global search capability of improved PSO is used to optimize the clustering center, and this significantly improves the accuracy and efficiency of the algorithm. Finally, a nonlinear system is modeled and simulated. The simulation results show the effectiveness and superiority of this method.

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备注/Memo

备注/Memo:
收稿日期:2017-07-13。
基金项目:河北省自然科学基金项目(2015203362).
作者简介:刘楠,男,1990年生,硕士研究生,主要研究方向为模糊辨识与控制、智能优化算法。;刘福才,男,1966年生,教授,博士生导师,主要研究方向为模糊辨识与预测控制、电力拖动及其计算机控制。发表学术论文160余篇,出版专著1部。;孟爱文,女,1991年生,硕士研究生,主要研究方向为模糊辨识、多项式模糊正系统控制与稳定性分析。
通讯作者:刘福才.E-mail:lfc@ysu.edu.cn
更新日期/Last Update: 2019-04-25