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

Authors:
Li Feng Zhang;Quan Min Zhu;Ashley Longden
Affiliations:
Department of Economic Information Management, School of Information, Renmin University of China, Beijing, China;Faculty of Computing, Engineering and Mathematical Sciences, University of the West of England, Bristol, UK;Faculty of Computing, Engineering and Mathematical Sciences, University of the West of England, Bristol, UK
Venue:
IEEE Transactions on Neural Networks
Year:
2009

Citing 10
Cited 5

System identification: theory for the user

System identification: theory for the user
On covariance function tests used in system identification

Automatica (Journal of IFAC) - Identification and system parameter estimation
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control
Development of omni-directional correlation functions for nonlinear model validation

Automatica (Journal of IFAC)
A set of novel correlation tests for nonlinear system variables

International Journal of Systems Science
On global-local artificial neural networks for function approximation

IEEE Transactions on Neural Networks
Support Vector Machines for Nonlinear Kernel ARMA System Identification

IEEE Transactions on Neural Networks
Delayed Standard Neural Network Models for Control Systems

IEEE Transactions on Neural Networks
Neural Network Adaptive Control for a Class of Nonlinear Uncertain Dynamical Systems With Asymptotic Stability Guarantees

IEEE Transactions on Neural Networks

A generalized procedure in designing recurrent neural network identification and control of time-varying-delayed nonlinear dynamic systems

Neurocomputing
Online identification of the system order with ANARX structure

ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Evolutionary design of the closed loop control on the basis of NN-ANARX model using genetic algorithm

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
A new hyper-parameters selection approach for support vector machines to predict time series

ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Letters: A generalized indirect adaptive neural networks backstepping control procedure for a class of non-affine nonlinear systems with pure-feedback prototype

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

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 upgrades 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.