Sabeur Abid

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In this letter, a new approach for the learning process of multilayer feedforward neural network is introduced. This approach minimizes a modified form of the criterion used in the standard backpropagation algorithm. This criterion is based on the sum of the linear and the nonlinear quadratic errors of the output neuron. The quadratic linear error signal is(More)
In this paper, the Davidon Fletcher Powell (DFP) algorithm for nonlinear least squares is proposed to train multilayer perceptron (MLP). Applied on both a single output layer perceptron and MLP, we find that this algorithm is faster than the Marquardt-Levenberg (ML) algorithm known as the fastest algorithm used to train MLP until now. The number of(More)
In this paper a new technique for defect detection in gray-level textured images is proposed. The first step of the algorithm is devoted to compute the local homogeneity of each pixel to construct a new homogeneity image denoted as (H-image). The second step consists in dividing the H-image into squared blocks and applying the discrete cosine transform(More)
In this paper we have proposed a new defect detection algorithm based on local homogeneity and hotteling model to localize defects in various textures images. Firstly, the local homogeneity of each pixel is computed to construct a new homogeneity image denoted as (h-image). Then a wavelet transform, in order to extract features details, is applied. After,(More)
In this work a new method for image edge detection based on multilayer perceptron (MLP) is proposed. The method is based on updating a MLP to learn a set of contours drawn on a 3×3 grid and then take advantage of the network generalization capacity to detect different edge details even for very noisy images. The method is applied first to Gray scale(More)
The blind identification of a special class of nonlinear system is pursued in this paper. In particular a genetic algorithm is developed for blind identification of quadratic Volterra model excited by an unobservable input signal which can either be a stationary Gaussian process or an i.i.d process. This approach enables a nonlinear relationship between(More)
In this paper, a new approach to quadratic blind system identification is proposed. This approach enables a nonlinear relationship between model kernels and output cumulants up to the third order by means of Genetic Algorithm (GA). Simulation results are presented to show good performance of this approach.