Abstract
In this paper, one kind of system identification algorithm based on an improved radial basis function (RBF) neural networks is presented for nonlinear and non-Gaussian systems. Survival information potential (SIP) of identification errors is employed to constructed the performance index to train the RBF neural networks. The data driven system identification algorithm implemented by the proposed RBF neural networks is applied to obtain the equivalent model of wind-thermal integrated power systems. Compared with the traditional RBF neural networks and back-propagation (BP) neural networks based on mean square error (MSE) criteria, simulation results demonstrate that the proposed system identification algorithm can obtain better SFR models for wind-thermal integrated power systems.
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