Forward and backward least angle regression for nonlinear system identification

Long Zhang, Kang Li

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

A forward and backward least angle regression (LAR) algorithm is proposed to construct the nonlinear autoregressive model with exogenous inputs (NARX) that is widely used to describe a large class of nonlinear dynamic systems. The main objective of this paper is to improve model sparsity and generalization performance of the original forward LAR algorithm. This is achieved by introducing a replacement scheme using an additional backward LAR stage. The backward stage replaces insignificant model terms selected by forward LAR with more significant ones, leading to an improved model in terms of the model compactness and performance. A numerical example to construct four types of NARX models, namely polynomials, radial basis function (RBF) networks, neuro fuzzy and wavelet networks, is presented to illustrate the effectiveness of the proposed technique in comparison with some popular methods.
Original languageEnglish
Pages (from-to)94-102
Number of pages9
JournalAutomatica
Volume53
Early online date07 Jan 2015
DOIs
Publication statusPublished - Mar 2015

Fingerprint

Dive into the research topics of 'Forward and backward least angle regression for nonlinear system identification'. Together they form a unique fingerprint.

Cite this