Hierarchical Stochastic Gradient Algorithm and its Performance Analysis for a Class of Bilinear-in-Parameter Systems

被引：44
|
作者：
Ding, Feng [1 ]
Wang, Xuehai [2 ]
机构：
[1] Nanchang Hangkong Univ, Sch Informat Engn, Nanchang 330063, Jiangxi, Peoples R China
[2] Xinyang Normal Univ, Coll Math & Informat Sci, Xinyang 464000, Peoples R China
来源：
CIRCUITS SYSTEMS AND SIGNAL PROCESSING | 2017年 / 36卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Parameter estimation; Gradient search; Hierarchical identification; Performance analysis; Bilinear-in-parameter system; WIENER NONLINEAR-SYSTEMS; SQUARES IDENTIFICATION ALGORITHM; STATE-SPACE SYSTEMS; AUXILIARY MODEL; HAMMERSTEIN SYSTEMS; FILTERING TECHNIQUE; DYNAMICAL-SYSTEMS; NEWTON ITERATION; DELAY;
D O I：
10.1007/s00034-016-0367-7
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 considers the parameter identification for a special class of nonlinear systems, i.e., bilinear-in-parameter systems. Based on the hierarchical identification principle, a hierarchical stochastic gradient (HSG) estimation algorithm is presented. The basic idea is to decompose a bilinear-in-parameter system into two subsystems and to derive the HSG identification algorithm for estimating the system parameters by replacing the unknown variables in the information vectors with their estimates obtained at the previous time. The convergence analysis of the proposed algorithm indicates that the parameter estimation errors converge to zero under persistent excitation conditions. The simulation results show that the proposed algorithm is effective.
引用
收藏
页码：1393 / 1405
页数：13
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