Eitan Michael Azoff

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Neural network time series forecasting error comprises autocorrelation error, due to an imperfect model, and random noise, inherent in the data. Both problems are addressed here, the first using a two stage training, growth-network neuron: the autocorrelation error (ACE) neuron. The second is considered as a post-processing noise filtering problem. These(More)
The use of principal component analysis in preprocessing neural network input data is explored. Four preprocessing schemes are compared in an example problem, and the theoretical basis for the results are discussed. A preconditioning method for the principal components is introduced here, combining normalisation and improved conditioning. The techniques are(More)
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