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

A new imputation-based incomplete data-driven fuzzy modeling for accuracy improvement in ubiquitous computing applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sonia Goel ◽  
Meena Tushir
Keyword(s):  
Parameter Estimation ◽  
Missing Data ◽  
Incomplete Data ◽  
Model Identification ◽  
Fuzzy Model ◽  
New Method ◽  
Content Type ◽  
Ubiquitous Environment ◽  
Sugeno Model ◽  
Takagi Sugeno

Purpose In real-world decision-making, high accuracy data analysis is essential in a ubiquitous environment. However, we encounter missing data while collecting user-related data information because of various privacy concerns on account of a user. This paper aims to deal with incomplete data for fuzzy model identification, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features. Design/methodology/approach In this work, authors proposed a three-fold approach for fuzzy model identification in which imputation-based linear interpolation technique is used to estimate missing features of the data, and then fuzzy c-means clustering is used for determining optimal number of rules and for the determination of parameters of membership functions of the fuzzy model. Finally, the optimization of the all antecedent and consequent parameters along with the width of the antecedent (Gaussian) membership function is done by gradient descent algorithm based on the minimization of root mean square error. Findings The proposed method is tested on two well-known simulation examples as well as on a real data set, and the performance is compared with some traditional methods. The result analysis and statistical analysis show that the proposed model has achieved a considerable improvement in accuracy in the presence of varying degree of data incompleteness. Originality/value The proposed method works well for fuzzy model identification method, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features with varying degree of missing data as compared to some well-known methods.

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