An evolving T–S fuzzy model identification approach based on a special membership function and its application on pump-turbine governing system

2018 ◽  
Vol 69 ◽  
pp. 93-103 ◽  
Author(s):  
Chaoshun Li ◽  
Wen Zou ◽  
Nan Zhang ◽  
Xinjie Lai
Keyword(s):  
Membership Function ◽  
Model Identification ◽  
Fuzzy Model ◽  
Pump Turbine ◽  
Governing System ◽  
Identification Approach

scholarly journals T-S Fuzzy Systems Optimization Identification Based on FCM and PSO

2020 ◽  
Author(s):  
Yaxue Ren ◽  
Fucai Liu ◽  
Jingfeng Lv ◽  
Aiwen Meng ◽  
Yintang Wen
Keyword(s):  
Membership Function ◽  
Gaussian Function ◽  
Model Identification ◽  
Fuzzy Model ◽  
Pso Algorithm ◽  
Identification Accuracy ◽  
Fine Tuning ◽  
Recursive Least Squares ◽  
Identification Method ◽  
Two Parameters

Abstract The division of fuzzy space is very important in the identification of premise parameters and the Gaussian membership function is applied to the premise fuzzy set. However, the two parameters of Gaussian membership function, center and width, are not easy to be determined. In this paper, a novel T-S fuzzy model optimal identification method of optimizing two parameters of Gaussian function based on Fuzzy c-means (FCM) and particle swarm optimization (PSO) algorithm is presented. Firstly, we use FCM algorithm to determine the Gaussian center for rough adjustment. Then, under the condition that the center of Gaussian function is fixed, the PSO algorithm is used to optimize another adjustable parameter, the width of the Gaussian membership function, to achieve fine tuning, so as to complete the identification of prerequisite parameters of fuzzy model. In addition, the recursive least squares (RLS) algorithm is used to identify the conclusion parameters. Finally, the effectiveness of this method for T-S fuzzy model identification is verified by simulation examples, and the higher identification accuracy can be obtained by using the novel identification method described compared with other identification methods.

A T–S fuzzy model identification approach based on evolving MIT2-FCRM and WOS-ELM algorithm

2020 ◽  
Vol 92 ◽  
pp. 103653
Author(s):  
Chunyang Wei ◽  
Chaoshun Li ◽  
Chen Feng ◽  
Jianzhong Zhou ◽  
Yongchuan Zhang
Keyword(s):  
Model Identification ◽  
Fuzzy Model ◽  
Identification Approach

scholarly journals Robust T-S Fuzzy Model Identification Approach Based on FCRM Algorithm and L1-Norm Loss Function

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 33792-33805
Author(s):  
Nan Zhang ◽  
Xiaoming Xue ◽  
Xin Xia ◽  
Wei Jiang ◽  
Chu Zhang ◽  
...  
Keyword(s):  
Loss Function ◽  
Model Identification ◽  
Fuzzy Model ◽  
L1 Norm ◽  
Identification Approach

T–S Fuzzy Model Identification Based on a Novel Hyperplane-Shaped Membership Function

2017 ◽  
Vol 25 (5) ◽  
pp. 1364-1370 ◽  
Author(s):  
Chaoshun Li ◽  
Jianzhong Zhou ◽  
Li Chang ◽  
Zhengjun Huang ◽  
Yongchuan Zhang
Keyword(s):  
Membership Function ◽  
Model Identification ◽  
Fuzzy Model

scholarly journals T-S fuzzy systems optimization identification based on FCM and PSO

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Yaxue Ren ◽  
Fucai Liu ◽  
Jinfeng Lv ◽  
Aiwen Meng ◽  
Yintang Wen
Keyword(s):  
Membership Function ◽  
Gaussian Function ◽  
Model Identification ◽  
Fuzzy Model ◽  
Pso Algorithm ◽  
Identification Accuracy ◽  
Fine Tuning ◽  
Recursive Least Squares ◽  
Identification Method ◽  
Two Parameters

AbstractThe division of fuzzy space is very important in the identification of premise parameters, and the Gaussian membership function is applied to the premise fuzzy set. However, the two parameters of Gaussian membership function, center and width, are not easy to be determined. In this paper, based on Fuzzy c-means (FCM) and particle swarm optimization (PSO) algorithm, a novel T-S fuzzy model optimal identification method of optimizing two parameters of Gaussian function is presented. Firstly, we use FCM algorithm to determine the Gaussian center for rough adjustment. Then, under the condition that the center of Gaussian function is fixed, the PSO algorithm is used to optimize another adjustable parameter, the width of the Gaussian membership function, to achieve fine-tuning, so as to complete the identification of prerequisite parameters of fuzzy model. In addition, the recursive least squares (RLS) algorithm is used to identify the conclusion parameters. Finally, the effectiveness of this method for T-S fuzzy model identification is verified by simulation examples, and the higher identification accuracy can be obtained by using the novel identification method described compared with other identification methods.

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