Error bounds on the outputs of artificial neural networks

Abstract

Resolving the uncertainties associated with solutions obtained from artificial neural networks (ANNs) is a major concern for ANN researchers. Error bounds on the solutions are important because they are in integral part of verification and validation. In this research, stacked generalization (SG) is applied to provide error bounds for novel solutions obtained from ANNs. This work shows that SG can provide error bounds on ANN results. We have applied SG to nuclear power plant fault detection for verification of diagnoses provided by ANNs