A correlation-test-based validation procedure for identified neural networks.

In this study, an enhanced correlation-test-based validation procedure is developed to check the quality of identified neural networks in modeling of nonlinear systems. The new computation algorithm up grades the validation power by including a direct correlation test between residuals and delayed outputs that have been quoted indirectly in the most previous approaches. Furthermore, based on the new validation procedure, three guidelines are proposed in this study to help explain the validation results and the statistic properties of the residuals.