Miroslav B. Milovanović, Dragan S. Antić, Saša S. Nikolić, Miodrag D. Spasić, Staniša Lj. Perić, Marko T. Milojković

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This paper will present training data denoising procedure for neural network performance improvement. Performance improvement will be measured by evaluation criterion which is based on a training estimation error and signal strength factor. Strength factor will be obtained by applying denoising method on a default training signal. The method is based on a noise removal procedure performed on the original signal in a manner which is defined by the proposed algorithm. Ten different processed signals are obtained from the performed method on a default noisy signal. Those signals are then used as a training data for the nonlinear autoregressive neural network learning phase. Empirical comparisons are made at the end, and they show that the proposed denoising procedure is an effective way to improve network performances when the training set possesses the significant noise component.

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