期刊
SYSTEMS & CONTROL LETTERS
卷 56, 期 5, 页码 373-380出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.sysconle.2006.10.026
关键词
recursive identification; parameter estimation; least squares; multi-innovation identification; hierarchical identification; auxiliary model; convergence properties; stochastic gradient; Hammerstein models; Wiener models; Martingale convergence theorem
The difficulty in identification of a Hammerstein (a linear dynamical block following a memoryless nonlinear block) nonlinear output-error model is that the information vector in the identification model contains unknown variables-the noise-free (true) outputs of the system. In this paper, an auxiliary model-based least-squares identification algorithm is developed. The basic idea is to replace the unknown variables by the output of an auxiliary model. Convergence analysis of the algorithm indicates that the parameter estimation error consistently converges to zero under a generalized persistent excitation condition. The simulation results show the effectiveness of the proposed algorithms. (C) 2006 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据