Particle Swarm Optimization of T-S Fuzzy Model

2010 ◽  
pp. 447-452 ◽  
Author(s):  
Ming Ma ◽  
Li-biao Zhang ◽  
Yan Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Swarm Optimization

scholarly journals Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
S. Sakinah S. Ahmad ◽  
Witold Pedrycz
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Feature Reduction ◽  
Data Sets ◽  
Swarm Optimization

The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.

Fuzzy Predictive Control Using Particle Swarm Optimization: Application to SCARA Robot

2014 ◽  
Vol 527 ◽  
pp. 230-236
Author(s):  
Mohamed Laid Hadjili ◽  
Kamel Kara ◽  
Oussama Ait Sahed ◽  
Jamal Bouyanzar
Keyword(s):  
Particle Swarm Optimization ◽  
Predictive Control ◽  
Degrees Of Freedom ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Robot Arm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Compliant Assembly ◽  
Fuzzy Predictive Control

In this work a fuzzy model-based predictive control (FMPC) method that uses modified particle swarm optimization (PSO) is presented. The main objective of this work is the application of this method to the control of a Selective Compliant Assembly Robot Arm (SCARA) with four degrees of freedom (4-DOF).

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.

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