This paper focuses on the parameter estimation for radial basis function-based state-dependent autoregressive models with moving average noises (RBF-ARMA models). An extended projection algorithm is derived based on the negative gradient search. In order to reduce the sensitivity of the algorithm to noise and reduce the fluctuations of the parameter estimation errors, a modified extended stochastic gradient algorithm is proposed. By introducing a moving data window, a modified moving data window-based extended stochastic gradient algorithm is further developed to improve the parameter estimation accuracy. The simulation results show that the proposed algorithms can effectively estimate the parameters of the RBF-ARMA models.

本文关注于带有移动平均噪声的基于径向基函数的状态相关自回归模型（RBF-ARMA模型）的参数估计。基于负梯度搜索推导了一种扩展投影算法。为了降低算法对噪声的敏感度，减少参数估计误差的波动，提出了一种改进的扩展随机梯度算法。通过引入运动数据窗口，进一步开发了改进的基于运动数据窗口的扩展随机梯度算法，以提高参数估计的准确性。仿真结果表明，该算法可以有效地估计RBF-ARMA模型的参数。