Discrete-Time Neural Identifier for Linear Induction Motors
نویسندگان
چکیده
This paper focusses on a discrete-time neural identifier applied to a Linear Induction Motor (LIM) model, whose model is assumed to be unknown. This neural identifier is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with a novel algorithm based on extended Kalman filter (EKF) and particle swarm optimization (PSO), using an off-line series-parallel configuration. Experimental results are included in order to illustrate the applicability of the proposed scheme. Keywords–Linear Induction Motor, Recurrent high order neural networks, Kalman filtering learning, Discretetime nonlinear systems, Neural identifier.
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