F. BenHmida

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In this paper, a novel Takagi-Sugeno fuzzy model identification based on a new fuzzy c-regression model clustering algorithm and particle swarm optimization is presented. The main motivation for this work is to develop an identification procedure for nonlinear systems taking into account the noise. In addition, a new distance is used in the objective(More)
This paper introduces the particle-filtering(PF)based framework for fault diagnosis in non-linear systems and noise and disturbances being Gaussian. In this paper, we use the sequential Monte Carlo filtering approach where the complete posterior distribution of the estimates are represented through samples or particles as opposed to the mean and covariance(More)
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