A fuzzy C‐regression model algorithm using a new PSO algorithm
Summary In this paper, a new methodology is introduced for the identification of the parameters of the multiple‐input–multiple‐output local linear Takagi‐Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In...
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| Veröffentlicht in: | International journal of adaptive control and signal processing Jg. 32; H. 1; S. 115 - 133 |
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| Sprache: | Englisch |
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Bognor Regis
Wiley Subscription Services, Inc
01.01.2018
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| Abstract | Summary
In this paper, a new methodology is introduced for the identification of the parameters of the multiple‐input–multiple‐output local linear Takagi‐Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In order to overcome this problem, adaptive chaos particle swarm optimization is proposed to optimize the initial states of WRLS. This new algorithm is improved versions of the original particle swarm optimization algorithm. Finally, comparative experiments are designed to verify the validity of the proposed clustering algorithm and the Takagi‐Sugeno fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature. |
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| AbstractList | In this paper, a new methodology is introduced for the identification of the parameters of the multiple‐input–multiple‐output local linear Takagi‐Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In order to overcome this problem, adaptive chaos particle swarm optimization is proposed to optimize the initial states of WRLS. This new algorithm is improved versions of the original particle swarm optimization algorithm. Finally, comparative experiments are designed to verify the validity of the proposed clustering algorithm and the Takagi‐Sugeno fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature. Summary In this paper, a new methodology is introduced for the identification of the parameters of the multiple-input-multiple-output local linear Takagi-Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In order to overcome this problem, adaptive chaos particle swarm optimization is proposed to optimize the initial states of WRLS. This new algorithm is improved versions of the original particle swarm optimization algorithm. Finally, comparative experiments are designed to verify the validity of the proposed clustering algorithm and the Takagi-Sugeno fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature. Summary In this paper, a new methodology is introduced for the identification of the parameters of the multiple‐input–multiple‐output local linear Takagi‐Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In order to overcome this problem, adaptive chaos particle swarm optimization is proposed to optimize the initial states of WRLS. This new algorithm is improved versions of the original particle swarm optimization algorithm. Finally, comparative experiments are designed to verify the validity of the proposed clustering algorithm and the Takagi‐Sugeno fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature. |
| Author | Chaari, Abdelkader Soltani, Moez Taieb, Adel |
| Author_xml | – sequence: 1 givenname: Adel orcidid: 0000-0002-4719-1954 surname: Taieb fullname: Taieb, Adel email: taeibadel@live.fr organization: National Higher Engineering School of Tunis (ENSIT) – sequence: 2 givenname: Moez surname: Soltani fullname: Soltani, Moez organization: National Higher Engineering School of Tunis (ENSIT) – sequence: 3 givenname: Abdelkader surname: Chaari fullname: Chaari, Abdelkader organization: National Higher Engineering School of Tunis (ENSIT) |
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In this paper, a new methodology is introduced for the identification of the parameters of the multiple‐input–multiple‐output local linear... In this paper, a new methodology is introduced for the identification of the parameters of the multiple‐input–multiple‐output local linear Takagi‐Sugeno fuzzy... Summary In this paper, a new methodology is introduced for the identification of the parameters of the multiple-input-multiple-output local linear... |
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| SubjectTerms | Algorithms chaos adaptive particle swarm optimization Clustering fuzzy C‐regression model clustering algorithm identification multiple‐input multiple‐output Nonlinear systems Parameter identification Particle swarm optimization Regression models Takagi‐Sugeno fuzzy models |
| Title | A fuzzy C‐regression model algorithm using a new PSO algorithm |
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