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This paper proposes an indirect adaptive control method using neural network (NN) based on a variable learning rates (VLRs) combined with Taylor development (TD) for nonlinear systems. In the proposed control architecture, two neural network blocks are used both as an identifier and a controller. The tracking error and the identification error are used, respectively, to train the neural controller...
This paper proposes a fuzzy switching control of domicile apparatus feeding from a photovoltaic panel (PVP) and of the diesel energy system. The principle consists in managing connections of apparatus on one of the two sources according to criteria of optimization in view to extract the maximum of energy from the PVP without using storage batteries. The simulation had been done by using Matlab 7.9...
In this paper, an on-line identification method based on recurrent neural networks (RNN) proposed for multivariable nonlinear systems. This work is an extension of an on-line method for single-input single output system. The large number of input-output vectors is being considered. As the complexity and nonlinearity of the systems is treated. The effectiveness of the proposed algorithm applied to...
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