NARX Model Identification Using Correntropy Criterion in the Presence of Non-Gaussian Noise

Journal of control, automation and electrical systems

article

Araújo, Ícaro B. Q.¹  Guimarães, João P. F.²  Fontes, Aluísio I. R.³  Linhares, Leandro L. S.³  Martins, Allan M.²  Araújo, Fábio M. U.² 

[1] Computer Institute, Federal University of Alagoas;Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte;Federal Institute of Rio Grande do Norte

关键词: Nonlinear system identification;    Polynomial NARX models;    Model structure selection;    Non-Gaussian noise;    Maximum correntropy criterion;   

DOI  :  10.1007/s40313-019-00476-9

学科分类：自动化工程

来源: Springer

【 摘 要 】

In past years, the system identification area has emphasized the identification of nonlinear dynamic systems. In this field, polynomial nonlinear autoregressive with exogenous (NARX) models are widely used due to flexibility and prominent representation capabilities. However, the traditional identification algorithms used for model selection and parameter estimation with NARX models have some limitation in the presence of non-Gaussian noise, since they are based on second-order statistics that tightly depend on the assumption of Gaussianity. In order to solve this dependence, a novel identification method called simulation correntropy maximization with pruning (SCMP) based on information theoretic learning is introduced by this paper. Results obtained in non-Gaussian noise environment in three experiments (numerical, benchmark data set and measured data from a real plant) are presented to validate the performance of the proposed approach when compared to other similar algorithms previously reported in the literature, e.g., forward regression with orthogonal least squares and simulation error minimization with pruning. The proposed SCMP method has shown increased accuracy and robustness for three different experiments.

【 授权许可】

CC BY