论文标题
基于嵌入的点迭代基于递归算法，用于在线识别非线性回归模型
Embedded Point Iteration Based Recursive Algorithm for Online Identification of Nonlinear Regression Models
论文作者
论文摘要
本文介绍了一种新颖的在线识别算法，用于非线性回归模型。由于模型中存在非线性结构，在线识别问题是具有挑战性的。以前的作品通常忽略了非线性回归模型的特殊结构，其中参数可以分为线性部分和非线性部分。在本文中，我们基于分析可变投影（VP）算法的等效形式的非线性回归模型开发了一种有效的递归算法。通过引入嵌入式点迭代（EPI）步骤，提出的递归算法可以正确利用线性参数和非线性参数的耦合关系。另外，从理论上讲，我们证明所提出的算法是均方界定的。关于合成数据和现实世界时间序列的数值实验验证了所提出算法的高效率和鲁棒性。
This paper presents a novel online identification algorithm for nonlinear regression models. The online identification problem is challenging due to the presence of nonlinear structure in the models. Previous works usually ignore the special structure of nonlinear regression models, in which the parameters can be partitioned into a linear part and a nonlinear part. In this paper, we develop an efficient recursive algorithm for nonlinear regression models based on analyzing the equivalent form of variable projection (VP) algorithm. By introducing the embedded point iteration (EPI) step, the proposed recursive algorithm can properly exploit the coupling relationship of linear parameters and nonlinear parameters. In addition, we theoretically prove that the proposed algorithm is mean-square bounded. Numerical experiments on synthetic data and real-world time series verify the high efficiency and robustness of the proposed algorithm.
