ADAPTIVE MODEL SELECTION AND ESTIMATION FOR NONLINEAR-SYSTEMS USING A SLIDING DATA WINDOW

被引：21

作者：

LUO, W ^{[1
]}

BILLINGS, SA ^{[1
]}

机构：

[1] UNIV SHEFFIELD,DEPT AUTOMAT CONTROL & SYST ENGN,SHEFFIELD S1 4DU,S YORKSHIRE,ENGLAND

来源：

SIGNAL PROCESSING | 1995年 / 46卷 / 02期

关键词：

ADAPTIVE MODEL SELECTION; NONLINEAR SYSTEM;

D O I：

10.1016/0165-1684(95)00081-N

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：

A new algorithm which provides adaptive model selection and estimation on-line is derived based on the polynomial nonlinear ARMAX model (NARMAX). The algorithm uses rectangular windowing regression procedures where the forgetting factor is unity within a sliding data window. Variations in the model structure, or which terms are in the model, and the parameter estimates are tracked by using a sliding rectangular window based on Givens rotations. The algorithm which minimizes the loss function at every step by selecting significant regression variables and computing the corresponding parameter estimates provides an efficient adaptive procedure which can be applied in nonlinear signal processing applications. Simulated examples are included to demonstrate the performance of the new algorithm.

引用

页码：179 / 202

页数：24

共 19 条

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