Weighted Parameter Estimation for Hammerstein Nonlinear ARX Systems

被引：93

作者：

Ding, Jie ^{[1
]}

Cao, Zhengxin ^{[1
]}

Chen, Jiazhong ^{[1
]}

Jiang, Guoping ^{[1
]}

机构：

[1] Nanjing Univ Posts & Telecommun, Sch Automat & Artificial Intelligence, Jiangsu Engn Lab IoT Intelligent Robots, Nanjing 210023, Peoples R China

来源：

CIRCUITS SYSTEMS AND SIGNAL PROCESSING | 2020年 / 39卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Hammerstein system; ARX system; Multi-innovation identification; Particle-filtering technique; RECURSIVE-IDENTIFICATION; PERFORMANCE ANALYSIS; ESTIMATION ALGORITHM; DESIGN; MODEL;

D O I：

10.1007/s00034-019-01261-4

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper proposes parameter estimation algorithms for Hammerstein nonlinear ARX systems. By making full use of the current and previous input-output data of the system, a weighted multi-innovation stochastic gradient algorithm is presented to improve the convergence rate of identification. The innovation term in the traditional identification algorithms can be treated as a particle in the particle-filtering technique, and the weight of each innovation then can be computed according to their importance. The simulation results indicate that the algorithm can improve the accuracy of parameter estimation.

引用

页码：2178 / 2192

页数：15

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