An efficient identification scheme for a nonlinear polynomial NARX model

Nonlinear polynomial NARX (nonlinear autoregressive with exogenous inputs) model identification often faces the problem of the huge size of the candidate pool, which makes the "wrapper" structure selection algorithm worked at low efficiency. In this article, a correlation-based orthogonal forward selection (COFS) algorithm is proposed to select the necessary input variables so that the candidate pool thus formed becomes tractable. What is more, it is trunked by an importance index-based term selection method, where a multiobjective evolutionary algorithm (MOEA)-based structure selection algorithm could be used to identify the polynomial model efficiently. Two numerical simulations are carried out to show the effectiveness of the proposed method.