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

100%

Soltani M., Chaari A., Ben Hmida F.

International Journal of Applied Mathematics and Computer Science

2012

tom 22

nr 3

617-628

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.

Further results on robust fuzzy dynamic systems with LMI 𝓓-stability constraints

100%

Assawinchaichote W.

International Journal of Applied Mathematics and Computer Science

2014

tom 24

nr 4

785-794

This paper examines the problem of designing a robust $𝓗_∞$ fuzzy controller with 𝓓-stability constraints for a class of nonlinear dynamic systems which is described by a Takagi-Sugeno (TS) fuzzy model. Fuzzy modelling is a multi-model approach in which simple sub-models are combined to determine the global behavior of the system. Based on a linear matrix inequality (LMI) approach, we develop a robust $𝓗_∞$ fuzzy controller that guarantees (i) the 𝓛₂-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value, and (ii) the closed-loop poles of each local system to be within a specified stability region. Sufficient conditions for the controller are given in terms of LMIs. Finally, to show the effectiveness of the designed approach, an example is provided to illustrate the use of the proposed methodology.

Ograniczanie wyników

2 International Journal of Applied Mathematics and Computer Science

1 Assawinchaichote W.

1 Ben Hmida F.

1 Chaari A.

1 Soltani M.

1 2014

1 2012

Wyniki wyszukiwania

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

Further results on robust fuzzy dynamic systems with LMI 𝓓-stability constraints