This paper studies some parameter estimation algorithms for a class of nonlinear models with exponential terms, i.e., the radial basis function-based state-dependent autoregressive (RBF-AR) models. An Aitken-based multi-innovation stochastic gradient algorithm is presented for the RBF-AR models based on the Aitken method. Inspired by the decomposition-coordination principle of large systems, an Aitken-based hierarchical multi-innovation stochastic gradient algorithm is proposed by combining the decomposition technique with the Aitken method. The effectiveness of the proposed algorithms are validated through two simulation examples.

中文翻译：

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基于Aitken的指数项非线性模型的分解加速度估计算法

本文研究了一类具有指数项的非线性模型的参数估计算法，即基于径向基函数的状态相关自回归(RBF-AR)模型。针对基于Aitken方法的RBF-AR模型，提出了一种基于Aitken的多创新随机梯度算法。受大系统分解协调原理的启发，将分解技术与Aitken方法相结合，提出了一种基于Aitken的分层多创新随机梯度算法。通过两个仿真实例验证了所提出算法的有效性。