Gradient-based iterative identification for nonuniform sampling output error systems

doi:10.1177/1077546310364993

Gradient-based iterative identification for nonuniform sampling output error systems

2010-11-08

0

PRE

AI

DOI：10.1177/1077546310364993

原文链接

原文求助

摘要

En 中文

The state-space models are derived for nonuniformly sampled data systems and the corresponding transfer function models are obtained. Since the information vector in the identification model contains unmeasurable true outputs, the conventional stochastic gradient algorithm cannot be applied. In order to overcome this difficulty, the gradient-based iterative identification algorithms are presented by replacing the unmeasurable variables with their iterative estimates at the previous step. The simulation results show that the proposed algorithm has high estimation accuracy compared with the auxiliary model-based stochastic gradient algorithm.

Gradient-based iterative identification for nonuniform sampling output error systems

摘要

AI总结

期刊

机构

引用论文