A two‐step scheme for polynomial NARX model identification based on MOEA with prescreening process

Abstract

Polynomial NARX (nonlinear autoregressive with exogenous) model identification has received considerable attention in last three decades. However, in a high‐order nonlinear system, it is very difficult to obtain the model structure directly even with state‐of‐art algorithms, because the number of candidate monomial terms is huge and increases drastically as the model order increases. Motivated by this fact, in this research, the identification is performed in two steps: firstly a prescreening process is carried out to select a reasonable number of important monomial terms based on two kinds of the importance indices. Then, in the reduced searching space with only the selected important terms, multi‐objective evolutionary algorithm (MOEA) is applied to determine a set of significant terms to be included in the polynomial model with the help of independent validation data. In this way, the whole identification algorithm is implemented efficiently. Numerical simulations are carried out to demonstrate the effectiveness of the proposed identification method. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A randomised approach for NARX model identification based on a multivariate Bernoulli distribution

“…The studied systems are the already introduced S 1 and S 2 , plus some additional ones taken from the literature, denoted S 4 (Cheng, Wang, & Jinglu, 2011), S 5 (Wei & Billings, 2008), and S 6 (Piroddi & Spinelli, 2003):…”