Time-Varying System Identification Using an Ultra-Orthogonal Forward Regression and Multiwavelet Basis Functions With Applications to EEG. Academic Article uri icon

abstract

  • A new parametric approach is proposed for nonlinear and nonstationary system identification based on a time-varying nonlinear autoregressive with exogenous input (TV-NARX) model. The TV coefficients of the TV-NARX model are expanded using multiwavelet basis functions, and the model is thus transformed into a time-invariant regression problem. An ultra-orthogonal forward regression (UOFR) algorithm aided by mutual information (MI) is designed to identify a parsimonious model structure and estimate the associated model parameters. The UOFR-MI algorithm, which uses not only the observed data themselves but also weak derivatives of the signals, is more powerful in model structure detection. The proposed approach combining the advantages of both the basis function expansion method and the UOFR-MI algorithm is proved to be capable of tracking the change of TV parameters effectively in both numerical simulations and the real EEG data.

published proceedings

  • IEEE Trans Neural Netw Learn Syst

altmetric score

  • 0.25

author list (cited authors)

  • Li, Y., Cui, W., Guo, Y., Huang, T., Yang, X., & Wei, H.

citation count

  • 34

complete list of authors

  • Li, Yang||Cui, Wei-Gang||Guo, Yu-Zhu||Huang, Tingwen||Yang, Xiao-Feng||Wei, Hua-Liang

publication date

  • July 2018