Quasiconvexity analysis of the Hammerstein model

被引：8

作者：

Rasouli, Mohammad ^{[1
]}

Westwick, David ^{[2
]}

Rosehart, William ^{[2
]}

机构：

[1] Schulich Sch Engn, Dept Elect & Comp Engn, Calgary, AB, Canada

[2] Univ Calgary, Dept Elect & Comp Engn, Schulich Sch Engn, Calgary, AB, Canada

来源：

AUTOMATICA | 2014年 / 50卷 / 01期

关键词：

Hammerstein; Identification; Convex; Quasiconvex; SYSTEM-IDENTIFICATION; NONLINEAR-SYSTEMS; CONVERGENCE;

D O I：

10.1016/j.automatica.2013.11.004

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, the Hammerstein identification problem with correlated inputs is studied in a prediction error framework using separable least squares methods. Thus, the identification is recast as an optimization over the parameters used to describe the nonlinearity. A sufficient condition is derived that guarantees that the identification problem is quasiconvex with respect to the parameters that describe the nonlinearity. Simulations using both IID and correlated inputs are used to illustrate the result. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：277 / 281

页数：5

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