Kernel Regularization Based Volterra Series Identification Method for Time-delayed Nonlinear Systems with Unknown Structure

被引：0

作者：

Zhang, Yanxin ^{[1
]}

Zhang, Zili ^{[1
]}

Chen, Jing ^{[1
]}

Hu, Manfeng ^{[1
]}

机构：

[1] Jiangnan Univ, Sch Sci, Wuxi 214122, Peoples R China

来源：

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | 2023年 / 21卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Adam optimization; kernel-based method; nonlinear system; self-organized maps; time-delay; Volterra series; PARAMETER-ESTIMATION; ESTIMATION ALGORITHM; MODEL RECOVERY;

D O I：

10.1007/s12555-021-0935-x

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper develops a kernel regularization based Adam algorithm for nonlinear systems with unknown structure and time-delay by using self-organized maps. Based on the redundant rule method, a model pool constituted of several Volterra series is constructed which contains the true time-delayed model. Then, the Adam algorithm combining a self-organized maps technique is applied to iteratively estimate the time-delay and parameters. Furthermore, a kernel regularization method is introduced to deal with the curse of dimensionality, and by which a more simple Volterra model can be obtained. Experimental results show the effectiveness of proposed algorithm in estimating time-delay and parameters.

引用

页码：1465 / 1474

页数：10

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