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.

SummaryIn this paper, a new methodology is introduced for the identification of the parameters of the multiple-input-multiple-output local linear Takagi-Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In order to overcome this problem, adaptive chaos particle swarm optimization is proposed to optimize the initial states of WRLS. This new algorithm is improved versions of the original particle swarm optimization algorithm. Finally, comparative experiments are designed to verify the validity of the proposed clustering algorithm and the Takagi-Sugeno fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature. KEYWORDSchaos adaptive particle swarm optimization, fuzzy C-regression model clustering algorithm, identification, multiple-input multiple-output, Takagi-Sugeno fuzzy models

This article studies the problem of inappropriate parameter estimation for nonlinear system when the dataset is contaminated by noise based on fuzzy c-regression models. In comparison to the existing algorithms in the literature, the proposed method uses a generalized objective function that reduces the errors of partitioning datasets contaminated by noise, and as a consequence an accurate model is obtained. Indeed, it combines a modified version of possibilistic c-means procedure with fuzzy c-regression models. The weighted least squares method is exploited to identify the parameters contained in the consequent (THEN part). The results of this study demonstrate the effectiveness of the proposed method compared with other extended versions of the fuzzy c-regression model algorithm such as modified fuzzy c-regression model algorithm, possibilistic c-regression model and interval type-2 fuzzy c-regression model algorithm as well as other techniques existing in the literature.

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The purpose of this paper is to present a new design for an optimal fuzzy sliding mode control based on a modified parallel distributed compensator and using a scalar sign function method. The proposed fuzzy sliding mode control uses a modified parallel distributed compensator scheme to find the optimal gains. To do this, the control gains are not considered constant through the linearized subsystem. Among these, we find state feedback gains, which are determined in offline mode using some prescribed performance criteria. Moreover, the fuzzy sliding surface of the system is designed using a stable eigenvector and the scalar sign function. The advantages of the proposed design are minimum energy control effort, faster response, and zero steady‐state error. We analyze and test the performance and stability of the new optimal fuzzy sliding mode control using simulation results that show that the proposed approach is very effective.

This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO) Fuzzy Optimal Model Predictive Control (FOMPC) using the Adaptive Particle Swarm Optimization (APSO) algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS) fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR) and Tank system, where the proposed approach provides better performances compared with other methods.

Purpose -The purpose of this paper is to present a new methodology for identification of the parameters of the local linear Takagi-Sugeno fuzzy models using weighted recursive least squares. The weighted recursive least squares (WRLS) is sensitive to initialization which leads to no converge. In order to overcome this problem, Euclidean particle swarm optimization (EPSO) is employed to optimize the initial states of WRLS. Finally, validation results are given to demonstrate the effectiveness and accuracy of the proposed algorithm. A comparative study is presented. Validation results involving simulations of numerical examples and the liquid level process have demonstrated the practicality of the algorithm. Design/methodology/approach -A new method for nonlinear system modelling. The proposed algorithm is employed to optimize the initial states of WRLS algorithm in two phases of learning algorithm. Findings -The results obtained using this novel approach were comparable with other modeling approaches reported in the literature. The proposed algorithm is able to handle various types of modeling problems with high accuracy. Originality/value -In this paper, a new method is employed to optimize the initial states of WRLS algorithm in two phases of the learning algorithm.

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