  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.
