CASCADE NONLINEAR-SYSTEM IDENTIFICATION BY A NONPARAMETRIC METHOD

被引：16

作者：

GREBLICKI, W ^{[1
]}

PAWLAK, M ^{[1
]}

机构：

[1] UNIV MANITOBA, DEPT ELECT & COMP ENGN, WINNIPEG R3T 2N2, MB, CANADA

来源：

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE | 1994年 / 25卷 / 01期

关键词：

D O I：

10.1080/00207729408928949

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A discrete-time non-linear time series system of a cascade structure is identified. The system consists of a non-linear memoryless element followed by a dynamic linear subsystem. The non-linear characteristic is recovered with the help of the kernel regression estimate. The consistency of the estimate in the presence of correlated noise is examined. The rate of convergence as well as some variance reduction method are established. Data-driven techniques for selection of a smoothing parameter are discussed. The identification of a linear part of the system is also studied.

引用

页码：129 / 153

页数：25

共 37 条

[21] ASYMPTOTIC PROPERTIES OF INTEGRATED SQUARE ERROR AND CROSS-VALIDATION FOR KERNEL ESTIMATION OF A REGRESSION FUNCTION [J].