关键词: Hammerstein-Wiener模型, 偏差补偿递推最小二乘法, 鞅收敛定理, 收敛性, 参数辨识

Abstract: A bias-compensated recursive least squares (BCRLS) algorithm was proposed to identify the Hammerstein-Wiener model with process noise, i.e., the bias compensation was introduced into the recursive least squares algorithm. The parameter vector containing the product of the original system parameters was thus identified on-line. Then, the convergence of the algorithm was proved by the martingale convergence theorem. It was found that the estimation error of parameter product uniformly converges to zero during persistent excitation. Simulation results showed that the proposed method is better than the identification method by recursive least squares.