scholarly journals New Allied Fuzzy C-Means algorithm for Takagi-Sugeno fuzzy model identification

2013 ◽  
Author(s):  
Bouzbida Mohamed ◽  
Troudi Ahmed ◽  
Hassine Lassad ◽  
Chaari Abdelkader
Keyword(s):  
Model Identification ◽  
Fuzzy Model ◽  
Fuzzy C Means ◽  
Takagi Sugeno ◽  
Fuzzy C Means Algorithm

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 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

Music psychology using fuzzy models to promote mental health

2021 ◽  
pp. 1-10
Author(s):  
Shengnan Liu ◽  
Tingting Zhang
Keyword(s):  
Mental Health ◽  
Objective Function ◽  
Fuzzy Model ◽  
Process Data ◽  
Research Results ◽  
Music Psychology ◽  
Fuzzy C Means ◽  
Fuzzy Models ◽  
Mental Health Patients ◽  
Fuzzy C Means Algorithm

Music psychology can play an important role in the diagnosis and rehabilitation of mental health patients. Under the guidance of music psychology, this paper combines fuzzy models to process data, which effectively solves the problem that the number of categories in the fuzzy c-means algorithm needs to be manually given. The AFCC algorithm effectively combines the idea of semi-supervised clustering with the CA algorithm. Through two sets of must-link and cannot-link, this paper introduces the constraint penalty item into the objective function, which greatly improves the clustering accuracy. On this basis, this paper constructs a fuzzy model of psychological rehabilitation and diagnosis based on music psychology, designs experiments to verify the performance of this model, and conducts research results statistics from two aspects of diagnosis and rehabilitation. The research results show that the model constructed in this paper has certain practical effects.

Comparative study on textual data set using fuzzy clustering algorithms

Kybernetes ◽  
2016 ◽  
Vol 45 (8) ◽  
pp. 1232-1242 ◽  
Author(s):  
Rjiba Sadika ◽  
Moez Soltani ◽  
Saloua Benammou
Keyword(s):  
Comparative Study ◽  
Clustering Algorithms ◽  
Fuzzy Model ◽  
Data Sets ◽  
Data Set ◽  
Content Type ◽  
Fuzzy C Means ◽  
Textual Data ◽  
Tunisian Revolution ◽  
Fuzzy C Means Algorithm

Purpose The purpose of this paper is to apply the Takagi-Sugeno (T-S) fuzzy model techniques in order to treat and classify textual data sets with and without noise. A comparative study is done in order to select the most accurate T-S algorithm in the textual data sets. Design/methodology/approach From a survey about what has been termed the “Tunisian Revolution,” the authors collect a textual data set from a questionnaire targeted at students. Five clustering algorithms are mainly applied: the Gath-Geva (G-G) algorithm, the modified G-G algorithm, the fuzzy c-means algorithm and the kernel fuzzy c-means algorithm. The authors examine the performances of the four clustering algorithms and select the most reliable one to cluster textual data. Findings The proposed methodology was to cluster textual data based on the T-S fuzzy model. On one hand, the results obtained using the T-S models are in the form of numerical relationships between selected keywords and the rest of words constituting a text. Consequently, it allows the authors to interpret these results not only qualitatively but also quantitatively. On the other hand, the proposed method is applied for clustering text taking into account the noise. Originality/value The originality comes from the fact that the authors validate some economical results based on textual data, even if they have not been written by experts in the linguistic fields. In addition, the results obtained in this study are easy and simple to interpret by the analysts.

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