Improved least‐squares identification for multiple‐output non‐linear stochastic systems

Abstract

This study considers the identification problems of multiple-output non-linear equation-error moving average systems. There exist the product items of the parameters between the non-linear and linear parts. To solve this difficulty, the key term separation technique is adopted. By using the model decomposition technique and the hierarchical identification principle, a maximum likelihood-based recursive extended least-squares estimation algorithm with reduced computational complexity is presented to estimate the parameters of the non-linear part and the linear part interactively. The simulation results demonstrate the effectiveness of the proposed method.