Blind Signal Separation Methods for Integration of Neural Networks Results

In this paper it is proposed to apply blind signal separation methods to improve a neural network prediction. Results generated by any regression model usually include both constructive and destructive components. In case of a few models, some of the components can be common to all of them. Our aim is to find the basis elements and distinguish the components with the constructive influence on the modelling quality from the destructive ones. After rejecting the destructive elements from the models results it is observed the enhancement of the results in terms of some standard error criteria. The validity and high performance of the concept is presented on the real problem of energy load prediction

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