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

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.

Fuzzy c-regression models based on Euclidean particle swarm optimization in noisy environment

“…Until err ≤ ε, then stop. Otherwise set l = l + 1 and return to Step 1. Like those in [7], {y(k − 1), y(k − 2), u(k), u(k − 1)} are chosen as input variables. The number of fuzzy rules is four.…”

“…The EPSO-FCRM algorithm uses two phases of learning algorithms to construct fuzzy models. In the first phase, the EPSO algorithm is used to search the optimal parameters of the fuzzy model by minimizing a defined objective function and is given as [7]:…”

“…Later, Li et al [5] as well as Li et al [6] utilized new FCRM clustering algorithm (NFCRMA) to nonlinear complex system identification. Recently, Soltani et al [7] proposed a new method of FCRM algorithm (PSO-NFCRM) using a modified objective function, a new error measure, and a parameter estimation based particle swarm optimization, in order to take into consideration the noisy data and the sensitive to initialization. However, the PSO can be easily trapped in local optima and premature convergence [8].…”

A Heuristic Group Intelligence Control System Based on Hybrid Particle Swarm Gravitational Search Algorithm

“…For example, the proposed hybrid particle swarm gravitational search algorithm which adopting co-evolutionary techniques to update the IGSA acceleration and particle positions with IPSO velocite simultaneously [5]. In the queue robot make decisions independently, through the coordination and cooperation between each other from the current position to determine their next position [12].…”

Fuzzy Supervisor Approach Design Based‐Switching Controller for Pumping Station: Experimental Validation

“…Moreover, it contains many variables which are too vague to model. In order to build an accurate model for the pumping station system, several algorithms based on Takagi-Sugeno (T-S) fuzzy model [1][2][3][4][5][6][7] have been carried out recently to identify the parameters for "black-box" systems using input-output data sets, among them the Fuzzy-C Means (FCM) algorithm [8][9][10][11][12][13][14][15][16]. The latter is particularly the most effective technique that can be used in nonlinear systems identification.…”