System Identification Using A Coefficient Learning Mechanism Via A Hopfield Neural Network
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چکیده
In this paper, an identification method is proposed for discrete-time nonlinear systems using a Hopfield neural network (HNN) as a coefficient learning mechanism to obtain optimized coefficients over a set of Gaussian basis functions. The outputs of the HNN, which are coefficients over a set of Gaussian basis functions, are discretized to be a discrete Hopfield learning model and completely approximated by the learning model if the sampled step size approaches zero. The main contribution of this paper is that the convergence condition of the discrete Hopfield learning model is derived. Finally, to demonstrate the effectiveness of the proposed methods, simulation results are illustrated in this paper.
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