OptiFel: A Convergent Heterogeneous Particle Swarm Optimization Algorithm for Takagi-Sugeno Fuzzy Modeling
Data-driven design of accurate and reliable Takagi-Sugeno (T-S) fuzzy systems has attracted a lot of attention, where the model structures and parameters are important and often solved in an optimization framework. The particle swarm optimization (PSO) algorithm is widely applied in the field. Howev...
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| Published in | IEEE transactions on fuzzy systems Vol. 22; no. 4; pp. 919 - 933 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.08.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1063-6706 1941-0034 |
| DOI | 10.1109/TFUZZ.2013.2278972 |
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| Abstract | Data-driven design of accurate and reliable Takagi-Sugeno (T-S) fuzzy systems has attracted a lot of attention, where the model structures and parameters are important and often solved in an optimization framework. The particle swarm optimization (PSO) algorithm is widely applied in the field. However, the classical PSO suffers from premature convergence, and it is trapped easily into local optima, which will significantly affect the model accuracy. To overcome these drawbacks, we have developed a new T-S fuzzy system parameters searching strategy called OptiFel with a heterogeneous multiswarm PSO (MsPSO) to enhance the searching performance. MsPSO groups the whole population into multiple cooperative subswarms, which perform different search behaviors for the potential solutions. We have found that the multiple subswarms strategy proposed in this paper is greatly helpful for finding the optimal parameters suitable for the subspaces of the T-S fuzzy model. Our theoretical proof has also demonstrated that the cooperation among the subswarms can maintain a balance between exploration and exploitation to ensure the particles converge to stable points. Experimental results show that MsPSO performs significantly better than traditional PSO algorithms on six benchmark functions. With the improved MsPSO, OptiFel can generate a good fuzzy system model with high accuracy and strong generalization ability. |
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| AbstractList | Data-driven design of accurate and reliable Takagi-Sugeno (T-S) fuzzy systems has attracted a lot of attention, where the model structures and parameters are important and often solved in an optimization framework. The particle swarm optimization (PSO) algorithm is widely applied in the field. However, the classical PSO suffers from premature convergence, and it is trapped easily into local optima, which will significantly affect the model accuracy. To overcome these drawbacks, we have developed a new T-S fuzzy system parameters searching strategy called OptiFel with a heterogeneous multiswarm PSO (MsPSO) to enhance the searching performance. MsPSO groups the whole population into multiple cooperative subswarms, which perform different search behaviors for the potential solutions. We have found that the multiple subswarms strategy proposed in this paper is greatly helpful for finding the optimal parameters suitable for the subspaces of the T-S fuzzy model. Our theoretical proof has also demonstrated that the cooperation among the subswarms can maintain a balance between exploration and exploitation to ensure the particles converge to stable points. Experimental results show that MsPSO performs significantly better than traditional PSO algorithms on six benchmark functions. With the improved MsPSO, OptiFel can generate a good fuzzy system model with high accuracy and strong generalization ability. |
| Author | Shen, Hong-Bin Ding, Xue-Ming Cheung, Ngaam J. |
| Author_xml | – sequence: 1 givenname: Ngaam J. surname: Cheung fullname: Cheung, Ngaam J. email: ngaam.ch@gmail.com organization: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China – sequence: 2 givenname: Xue-Ming surname: Ding fullname: Ding, Xue-Ming email: xuemingding@usst.edu.cn organization: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai , China – sequence: 3 givenname: Hong-Bin surname: Shen fullname: Shen, Hong-Bin email: hbshen@sjtu.edu.cn organization: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China |
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| SubjectTerms | Algorithms Computational modeling Convergence Convergence analysis Fuzzy Fuzzy logic Fuzzy set theory Fuzzy systems heterogeneous search Mathematical model Mathematical models OptiFel Optimization Particle swarm optimization particle swarm optimization (PSO) Searching Strategy Swarm intelligence Takagi-Sugeno (T-S) fuzzy system Vectors |
| Title | OptiFel: A Convergent Heterogeneous Particle Swarm Optimization Algorithm for Takagi-Sugeno Fuzzy Modeling |
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