A Novel Fuzzy Identification Method Based on Ant Colony Optimization Algorithm

被引：37

作者：

Tsai, Shun-Hung ^{[1
]}

Chen, Yu-Wen ^{[1
]}

机构：

[1] Natl Taipei Univ Technol, Grad Inst Automat Technol, Taipei 10608, Taiwan

来源：

IEEE ACCESS | 2016年 / 4卷

关键词：

Fuzzy system identification; Takagi-Sugeno fuzzy model; ant colony optimization algorithm (ACO); fuzzy c-regression model; PARTICLE SWARM OPTIMIZATION; SYSTEM-IDENTIFICATION; CLUSTERING-ALGORITHM; C-MEANS; MODEL; CRITERION; NETWORKS; VALIDITY;

D O I：

10.1109/ACCESS.2016.2585670

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, an identification problem for nonlinear models is explored and a novel fuzzy identification method based on the ant colony optimization algorithm is proposed. First, a modified cluster validity criterion with a fuzzy c-regression model is adopted to find appropriate rule numbers of the Takagi-Sugeno fuzzy model. Then, the ant colony optimization algorithm is adopted and the sifted initial membership function and the consequent parameters of the fuzzy model are obtained. Through an improved fuzzy c-regression model and the orthogonal least-squares method, the premise structure and the consequent parameters can be obtained to establish the Takagi-Sugeno fuzzy model. Some examples are illustrated to show that the proposed method provides better approximation results and robustness than those obtained using some of the existing methods.

引用

页码：3747 / 3756

页数：10

共 59 条

[1] Quantum-Assisted Routing Optimization for Self-Organizing Networks [J].

Alanis, Dimitrios ;

Botsinis, Panagiotis ;

Ng, Soon Xin ;

Hanzo, Lajos .

IEEE ACCESS, 2014, 2 :614-632

[2]

[Anonymous], 1997, IEEE T AUTOM CONTROL, DOI DOI 10.1109/TAC.1997.633847

[3]

[Anonymous], Pattern Recognition with Fuzzy Objective Function Algorithms, DOI 10.1007/978-1-4757-0450-1_3

[4]

Bezdek J.C., 1973, Cluster validity with fuzzy sets, P58

[5] A Power-Efficient Adaptive Fuzzy Resolution Control System for Wireless Body Sensor Networks [J].

Chen, Shih-Lun .

IEEE ACCESS, 2015, 3 :743-751

[6] Identification of fuzzy models using a successive tuning method with a variant identification ratio [J].

Choi, Jeoung-Nae ;

Oh, Sung-Kwun ;

Pedrycz, Witold .

FUZZY SETS AND SYSTEMS, 2008, 159 (21) :2873-2889

[7] Modeling and Optimizing Tensile Strength and Yield Point on a Steel Bar Using an Artificial Neural Network With Taguchi Particle Swarm Optimizer [J].

Chou, Ping-Yi ;

Tsai, Jinn-Tsong ;

Chou, Jyh-Horng .

IEEE ACCESS, 2016, 4 :585-593

[8]

Czerniak JM, 2014, COMM COM INF SC, V424, P259

[9] A fuzzy hybrid hierarchical clustering method with a new criterion able to find the optimal partition [J].

Devillez, A ;

Billaudel, P ;

Lecolier, GV .

FUZZY SETS AND SYSTEMS, 2002, 128 (03) :323-338

[10]

Dorigo M., 1997, IEEE Transactions on Evolutionary Computation, V1, P53, DOI 10.1109/4235.585892

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