Fitting jump models

被引：40

作者：

Bemporad, Alberto ^{[1
]}

Breschi, Valentina ^{[2
]}

Piga, Dario ^{[3
]}

Boyd, Stephen P. ^{[4
]}

机构：

[1] IMT Sch Adv Studies Lucca, Piazza San Francesco 19, I-55100 Lucca, Italy

[2] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, Piazza L Da Vinci 32, I-20133 Milan, Italy

[3] Dalle Molle Inst Artificial Intelligence Res USI, Galleria 2,Via Cantonale 2c, CH-6928 Manno, Switzerland

[4] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA

来源：

AUTOMATICA | 2018年 / 96卷

关键词：

Model regression; Mode estimation; Jump models; Hidden Markov models; Piecewise affine models; IDENTIFICATION;

D O I：

10.1016/j.automatica.2018.06.022

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We describe a new framework for fitting jump models to a sequence of data. The key idea is to alternate between minimizing a loss function to fit multiple model parameters, and minimizing a discrete loss function to determine which set of model parameters is active at each data point. The framework is quite general and encompasses popular classes of models, such as hidden Markov models and piecewise affine models. The shape of the chosen loss functions to minimize determines the shape of the resulting jump model. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：11 / 21

页数：11

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