This article studies the parameter estimation problems of radial basis function‐based state‐dependent autoregressive models with autoregressive noises (RBF‐ARAR models). To reduce the effect of the colored noise to parameter estimation, the data filtering technique is applied and a filtering based generalized stochastic gradient algorithm is derived for the RBF‐ARAR models. In order to achieve more accurate parameter estimates, a filtering based multiinnovation generalized stochastic gradient (F‐MI‐GSG) algorithm is proposed by utilizing the current and past innovations. Introducing two forgetting factors, a filtering based multiinnovation generalized forgetting gradient algorithm is developed to improve the transient performance of the F‐MI‐GSG algorithm. The effectiveness of the proposed algorithms is verified through the simulation examples.

中文翻译：

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带有滤波算法和多元创新理论的带有自回归噪声的基于非线性径向基函数的状态依赖自回归网络的参数辨识

本文研究带有自回归噪声的基于径向基函数的状态相关自回归模型的参数估计问题（RBF‐ARAR模型）。为了减少彩色噪声对参数估计的影响，应用了数据过滤技术，并针对RBF-ARAR模型推导了基于过滤的广义随机梯度算法。为了获得更准确的参数估计，通过利用当前和过去的创新，提出了一种基于滤波的多创新广义随机梯度（F-MI-GSG）算法。引入两个遗忘因素，开发了一种基于滤波的多创新广义遗忘梯度算法，以提高F-MI-GSG算法的瞬态性能。通过仿真实例验证了所提算法的有效性。