NONLINEAR DILATION NETWORK FOR PREDICTION APPLICATIONS

Abstract This paper presents a neural network in which a nonlinear dilation function in the input layer is introduced to emulate the nonlinearity of human sensors, say, the ear. The introduction of the dilation function reduces the redundancy in the information contained in the input variables. This results in a minimization of the prediction error, as well as of the error variance. The applications of time-series predictions and the instantaneous VAr prediction of an electric arc furnace are included to corroborate the performance of the nonlinear dilation network.

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