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
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
A set of novel correlation tests for nonlinear system variables
International Journal of Systems Science
Paper: Maximum-power validation of models without higher-order fitting
Automatica (Journal of IFAC)
New correlation analysis method for nonstationary signal
WSEAS Transactions on Information Science and Applications
A correlation-test-based validation procedure for identified neural networks
IEEE Transactions on Neural Networks
Online identification of the system order with ANARX structure
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
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
Hi-index | 22.14 |
In the present study a set of first order correlation functions are proposed to examine the quality of a wide class of identified nonlinear models. The first order correlation functions, defined as omni-directional correlation functions, are integrated into two concise tests to provide more effective auto and cross model error correlation diagnosis than the other approaches proposed from higher order correlation functions. The mechanisms of the novel validity tests are proved in theory and demonstrated with numerical analyses. Two simulated case studies, in the situation of incorrectly detected model structure and estimated parameters, are presented to illustrate the diagnostic power of the new methodology.