[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(2):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(2):378-384.[doi:10.11992/tis.201707025]
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LIU Nan,LIU Fucai,MENG Aiwen.Fuzzy identification based on improved PSO and FCM[J].CAAI Transactions on Intelligent Systems,2019,14(2):378-384.[doi:10.11992/tis.201707025]
基于改进PSO和FCM的模糊辨识
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第2期
页码:
378-384
栏目:
学术论文—智能系统
出版日期:
2019-03-05
- Title:
- Fuzzy identification based on improved PSO and FCM
- Keywords:
- fuzzy identification; nonlinear system; fuzzy C-means; T-S model; intelligent algorithm; particle swarm optimization; Box-Jenkins identification; global optimization
- 分类号:
- TP15
- 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.
- 参考文献/References:
-
[1] TAKAGI T, SUGENO M. Fuzzy identification of systems and its applications to modeling and control[J]. IEEE transactions on systems, man, and cybernetics, 1985, SMC-15(1):116-132.
[2] PARK B J, OH S K, PEDRYCZ W. Fuzzy identification by means of partitions of fuzzy input space and an aggregate objective function[C]//IEEE International Fuzzy Systems Conference Proceedings. Seoul, South Korea, 1999, 1:480-485.
[3] 李峰磊, 窦金梅, 刘福才, 等. 一种基于改进BFO和RLS的模糊建模方法[J]. 南京理工大学学报, 2014, 38(2):252-258 LI Fenglei, DOU Jinmei, LIU Fucai, et al. Fuzzy modeling method with improved BFO and RLS[J]. Journal of Nanjing university of science and technology, 2014, 38(2):252-258
[4] LI C, WU T. Adaptive fuzzy approach to function approximation with PSO and RLSE[J]. Expert systems with applications, 2011, 38(10):13266-13273.
[5] TAMURA S, HIGUCHI S, TANAKA K. Pattern classification based on fuzzy relations[J]. IEEE transactions on systems, man, and cybernetics, 1971, SMC-1(1):61-66.
[6] BACKER E, JAIN A K. A clustering performance measure based on fuzzy set decomposition[J]. IEEE transactions on pattern analysis and machine intelligence, 1981, PAMI-3(1):66-75.
[7] 樊治平, 于春海, 尤天慧. 一种基于三角模糊数多指标信息的FCM聚类算法[J]. 控制与决策, 2004, 19(12):1407-1411 FAN Zhiping, YU Chunhai, YOU Tianhui. An FCM clustering algorithm for multiple attribute information with triangular fuzzy numbers[J]. Control and decision, 2004, 19(12):1407-1411
[8] WU Z, LEATHY R. An optimal graph theoretic approach to data clustering:theory and its application to image segmentation[J]. IEEE transactions on pattern analysis and machine intelligence, 1993, 15(11):1101-1113.
[9] DUNN J C. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters[J]. Journal of cybernetics, 1973, 3(3):32-57.
[10] BEZDEK J C. Numerical taxonomy with fuzzy sets[J]. Journal of mathematical biology, 1974, 1(1):57-71.
[11] KENNEDY J, EBERHART R C. Particle swarm optimization[C]//Proceedings of the IEEE International Conference on Neural Networks, Australia 1995:1942?1948.
[12] CHEN Jianqin, XI Yugeng, ZHANG Zhongjun. A clustering algorithm for fuzzy model identification[J]. Fuzzy sets and systems, 1998, 98(3):319-329.
[13] HATHAWAY R J, BEZDEK J C, TUCKER W T. Recent convergence results for the fuzzy C-means clustering algorithms[J]. Journal of classification, 1988, 5(2):237-247.
[14] BEZDEK J C, HATHAWAY R J, SABIN M J, et al. Convergence theory for fuzzy c-means:counterexamples and repairs[J]. IEEE transactions on systems, man, and cybernetics, 1987, 17(5):873-877.
[15] HATHAWAY R J, BEZDEK J C, TUCKER W T. Recent convergence results for the fuzzy C-means clustering algorithms[J]. Journal of classification, 1988, 5(2):237-247.
[16] BOX G E P, JENKINS A D. Time series analysis:forecasting and control[M]. San Francisco, USA:Holden-Day, 1976.
[17] YOSHINARI Y, PEDRYCZ W, HIROTA K. Construction of fuzzy models through clustering techniques[J]. Fuzzy sets and systems, 1993, 54(2):157-165.
[18] 王宏伟, 马广富, 王子才. 一种基于模糊规则的模糊辩识方法[J]. 系统仿真学报, 1998, 10(4):61-64 WANG Hongwei, MA Guangfu, WANG Zicai. A fuzzy identification method via fuzzy rules[J]. Acta simulata systematica sinica, 1998, 10(4):61-64
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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