摘要：AbstractThis article is concerned with the identification of autoregressive models with exogenous inputs (ARX). Most of the existing approaches like prediction error minimization and state-space framework are widely accepted and utilized to estimate ARX models but are known to deliver unbiased and consistent parameter estimates for a correctly supplied guess of input-output orders and delay. In this paper, we propose a novel automated spectral decomposition framework that recovers orders, delay, output noise distribution, along with parameter estimates. The proposed algorithm systematically estimates all the parameters in two steps. The first step estimates the order by examining the generalized eigenvalues, and the second step estimates the parameter from the generalized eigenvectors. Simulation studies are presented to demonstrate the proposed method’s efficacy and are observed to deliver consistent estimates even at low signal-to-noise ratio (SNR).
