title: A correlation-test-based validation procedure for identified neural networks.
abstract: 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. It is hoped that this study could promote awareness of why the correlation tests are an effective method of validating identified neural networks, and provide examples how to use the tests in user applications.
authors.affiliation: Department of Economic Information Management, School of Information, Renmin University of China, Beijing, China.
authors.forename: Li Feng
authors.initials: LF
authors.lastname: Zhang
authors.forename: Quan Min
authors.initials: QM
authors.lastname: Zhu
authors.forename: Ashley
authors.initials: A
authors.lastname: Longden
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results