Mohamed Chemachema

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A direct adaptive control algorithm, based on neural networks (NN) is presented for a class of single input single output (SISO) nonlinear systems. The proposed controller is implemented without a priori knowledge of the nonlinear systems; and only the output of the system is considered available for measurement. Contrary to the approaches available in the(More)
Face recognition is one of the most important tasks in computer vision and biometrics where many algorithms have been developed. The Local Binary Pattern (LBP) has been proved to be effective for facial image representation and analysis, but it is too local to be robust. In this paper, we present an improved method for face recognition named Elongated(More)
In this paper a classification algorithms MLP and SVM for the segmentation tissue of brain magnetic resonance images is proposed. Magnetic resonance imaging (MRI) segmentation is an important technique to differentiate abnormal and normal tissues in MR image data. The method interleaves classification with estimation of the model parameters, improving the(More)
This paper deals with indirect adaptive control using fuzzy systems for a class of uncertain SISO systems with unknown control gain sign. The uncertain nonlinearities of the systems are captured by fuzzy systems that have been proven to be universal approximators and the Nussbaum -type function is used to deal with the unknown control gain sign. The(More)
In this paper, the controller introduced represent, at the best knowledge of the authors, the first application of feedback linearization technique to control a Twin Rotor Multi-input Multi-output System (TRMS). With the coupling effects considered as the uncertainties, the highly coupled nonlinear TRMS is decomposed into a horizontal and a vertical(More)