System identification of nonlinear state-space models

被引：418
|
作者：
Schon, Thomas B. [1 ]
Wills, Adrian [2 ]
Ninness, Brett [2 ]
机构：
[1] Linkoping Univ, Div Automat Control, SE-58183 Linkoping, Sweden
[2] Univ Newcastle, Sch Elect Engn & Comp Sci, Callaghan, NSW 2308, Australia
来源：
AUTOMATICA | 2011年 / 47卷 / 01期
基金：
瑞典研究理事会; 澳大利亚研究理事会;
关键词：
System identification; Nonlinear models; Dynamic systems; Monte Carlo method; Smoothing filters; Expectation maximisation algorithm; Particle methods; PARAMETER-ESTIMATION; MAXIMUM-LIKELIHOOD; PARTICLE METHODS;
D O I：
10.1016/j.automatica.2010.10.013
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper is concerned with the parameter estimation of a general class of nonlinear dynamic systems in state-space form. More specifically, a Maximum Likelihood (ML) framework is employed and an Expectation Maximisation (EM) algorithm is derived to compute these ML estimates. The Expectation (E) step involves solving a nonlinear state estimation problem, where the smoothed estimates of the states are required. This problem lends itself perfectly to the particle smoother, which provides arbitrarily good estimates. The maximisation (M) step is solved using standard techniques from numerical optimisation theory. Simulation examples demonstrate the efficacy of our proposed solution. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码：39 / 49
页数：11
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