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In this paper, we address the problem of tracking multiple targets trajectories that potentially cross each other. We propose a solution to this problem by using the support vector machines (SVM) to predict the position of the targets from the past history of the measurements. We will predict the dynamic behaviour of the targets using the SVM method, after turning off the sensing mode. By this way,...
In this paper, a nonlinear system identification based on support vector machines (SVM) has been addressed. A family of SVM-ARMA models is presented in order to integrate the input and the output in the reproducing kernel Hilbert space (RKHS). The performances of the different SVM-ARMA formulations for system identification are illustrated with two systems and compared with the least square method.
In this paper a non linear system identification problem is addressed. A support vector regressor is used to solve the Internet traffic identification problem. We give a basic idea underlying support vector (SV) machine for regression, which is a novel type of learning machine based on statistical learning theory. Furthermore, we describe how SV regressor can be applied for non linear system identification...
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