Takagi-Sugeno fuzzy model parameters identification based on fuzzy c-regression model clustering algorithm and particle swarm optimization

2012 ◽  
Author(s):  
Moez Soltani ◽  
Abdelkader Chaari ◽  
Faycal BenHmida
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Parameters Identification ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Takagi Sugeno

scholarly journals A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization

2012 ◽  
Vol 22 (3) ◽  
pp. 617-628 ◽  
Author(s):  
Moêz Soltani ◽  
Abdelkader Chaari ◽  
Fayçal Ben Hmida
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Objective Function ◽  
Clustering Algorithm ◽  
Least Squares Method ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Unknown Parameters ◽  
Swarm Optimization ◽  
Error Measure

Abstract This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained fuzzy model. The orthogonal least squares method is used to identify the unknown parameters of the local linear model. Finally, validation results of two examples are given to demonstrate the effectiveness and practicality of the proposed algorithm.

scholarly journals Training High-Order Takagi-Sugeno Fuzzy Systems Using Batch Least Squares and Particle Swarm Optimization

2019 ◽  
Vol 22 (1) ◽  
pp. 22-34 ◽  
Author(s):  
Krzysztof Wiktorowicz ◽  
Tomasz Krzeszowski
Keyword(s):  
Particle Swarm Optimization ◽  
Least Squares ◽  
Fuzzy Sets ◽  
Fuzzy Systems ◽  
Piecewise Linear ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Takagi Sugeno

AbstractThis paper proposes two methods for training Takagi–Sugeno (T-S) fuzzy systems using batch least squares (BLS) and particle swarm optimization (PSO). The T-S system is considered with triangular and Gaussian membership functions in the antecedents and higher-order polynomials in the consequents of fuzzy rules. In the first method, the BLS determines the polynomials in a system in which the fuzzy sets are known. In the second method, the PSO algorithm determines the fuzzy sets, whereas the BLS determines the polynomials. In this paper, the ridge regression is used to stabilize the solution when the problem is close to the singularity. Thanks to this, the proposed methods can be applied when the number of observations is less than the number of predictors. Moreover, the leave-one-out cross-validation is used to avoid overfitting and this way to choose the structure of a fuzzy model. A method of obtaining piecewise linear regression by means of the zero-order T-S system is also presented.

A PSO-Based Fuzzy c-Regression Model Applied to Nonlinear Data Modeling

2015 ◽  
Vol 23 (06) ◽  
pp. 881-891 ◽  
Author(s):  
Moez Soltani ◽  
Abdelkader Chaari
Keyword(s):  
Particle Swarm Optimization ◽  
Least Squares ◽  
Regression Models ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Recursive Least Squares ◽  
Unknown Parameters ◽  
Identification Procedure ◽  
Swarm Optimization

This paper presents a new method for fuzzy c-regression models clustering algorithm. The main motivation for this work is to develop an identification procedure for nonlinear systems using weighted recursive least squares and particle swarm optimization. The fuzzy c-regression models algorithm is sensitive to initialization which leads to the convergence to a local minimum of the objective function. In order to overcome this problem, particle swarm optimization is employed to achieve global optimization of FCRM and to finally tune parameters of obtained fuzzy model. The weighted recursive least squares is used to identify the unknown parameters of the local linear model. Finally, validation results involving simulation of two examples have demonstrated the effectiveness and practicality of the proposed algorithm.

scholarly journals A New Composite Lognormal-Pareto Type II Regression Model to Analyze Household Budget Data via Particle Swarm Optimization

2021 ◽  
Author(s):  
Hande Konşuk Ünlü
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Regression Models ◽  
Particle Swarm ◽  
Model Parameters ◽  
Type Ii ◽  
Swarm Optimization ◽  
Household Budget Survey ◽  
Proposed Model ◽  
Household Budget

Abstract When data exhibits heavy-tailed behavior, traditional regression approaches might be inadequate or inappropriate to model the data. In such data analyses, composite models, which are built by piecing together two or more weighted distributions at specified threshold(s), are alternative models. When data contain covariate information, composite regression models can be used. In the existing literature, there is not much work done on this topic. The only study is Gan and Valdez (2018)'s paper. In this study, a novel Lognormal-Pareto Type II composite regression model is proposed. Particle swarm optimization ( PSO ) is performed to obtain model parameters of the proposed model. The proposed model is applied to model monthly consumption expenditure and affecting factors. The data is obtained from the National Household Budget Survey, which is conducted annually by the Turkish Statistical Institute ( TurkStat ). Since the sampling design of the Household Budget Survey is stratified two-stage cluster sampling, the parameters are estimated under weighted data by updating the proposed model and PSO . Additionally, the proposed regression model performance is compared with Lognormal , Lomax , Gamma and Gamma-Pareto type II regression models. The results demonstrate that the proposed model provides an improved fit to data.

Statistical process monitoring for e-waste based on beta regression and particle swarm optimization

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Angelo Marcio Oliveira Sant’Anna
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Particle Swarm ◽  
Injection Rate ◽  
Statistical Process ◽  
Swarm Optimization ◽  
Content Type ◽  
Statistical Process Monitoring

PurposeE-waste management can reduce relevant impact of the business activity without affecting reliability, quality or performance. Statistical process monitoring is an effective way for managing reliability and quality to devices in manufacturing processes. This paper proposes an approach for monitoring the proportion of e-waste devices based on Beta regression model and particle swarm optimization. A statistical process monitoring scheme integrating residual useful life techniques for efficient monitoring of e-waste components or equipment was developed.Design/methodology/approachAn approach integrating regression method and particle swarm optimization algorithm was developed for increasing the accuracy of regression model estimates. The control chart tools were used for monitoring the proportion of e-waste devices from fault detection of electronic devices in manufacturing process.FindingsThe results showed that the proposed statistical process monitoring was an excellent reliability and quality scheme for monitoring the proportion of e-waste devices in toner manufacturing process. The optimized regression model estimates showed a significant influence of the process variables for both individually injection rate and toner treads and the interactions between injection rate, toner treads, viscosity and density.Originality/valueThis research is different from others by providing an approach for modeling and monitoring the proportion of e-waste devices. Statistical process monitoring can be used to monitor waste product in manufacturing. Besides, the key contribution in this study is to develop different models for fault detection and identify any change point in the manufacturing process. The optimized model used can be replicated to other Electronic Industry and allows support of a satisfactory e-waste management.

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