Recursive Fuzzy Model Identification

2010 ◽  
Author(s):  
D. Dovžan ◽  
I. Škrjanc
Keyword(s):  
Model Identification ◽  
Fuzzy Model

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.

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.

Exploiting Prior Knowledge in Fuzzy Model Identification of a Heat Exchanger

1997 ◽  
Vol 30 (25) ◽  
pp. 391-396 ◽  
Author(s):  
Martin Fischer ◽  
Oliver Nelles ◽  
Rolf Isermann
Keyword(s):  
Heat Exchanger ◽  
Prior Knowledge ◽  
Model Identification ◽  
Fuzzy Model

scholarly journals Takagi-Sugeno fuzzy model identification for turbofan aero-engines with guaranteed stability

2018 ◽  
Vol 31 (6) ◽  
pp. 1206-1214 ◽  
Author(s):  
Ruichao LI ◽  
Yingqing GUO ◽  
Sing Kiong NGUANG ◽  
Yifeng CHEN
Keyword(s):  
Model Identification ◽  
Fuzzy Model ◽  
Guaranteed Stability ◽  
Aero Engines ◽  
Takagi Sugeno

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
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