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

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.