Piecewise nonlinear regression with data augmentation

被引：4
|
作者：
Mazzoleni, M. [1 ]
Breschi, V [2 ]
Formentin, S. [2 ]
机构：
[1] Univ Bergamo, Dept Management Informat & Prod Engn, Via Marconi 5, I-24044 Dalmine, BG, Italy
[2] Politecn Milan, Dept Elect Informat & Bioengn, Via G Ponzio 34-5, I-20133 Milan, Italy
来源：
IFAC PAPERSONLINE | 2021年 / 54卷 / 07期
关键词：
Hybrid System Identification; Nonparametric Methods; Nonlinear System Identification; SYSTEM-IDENTIFICATION;
D O I：
10.1016/j.ifaco1.2021.08.396
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Piecewise regression represents a powerful tool to derive accurate yet modular models describing complex phenomena or physical systems. This paper presents an approach for learning PieceWise NonLinear (PWNL) functions in both a supervised and semi-supervised setting. We further equip the proposed technique with a method for the automatic generation of additional unsupervised data, which are leveraged to improve the overall accuracy of the estimate. The performance of the proposed approach is preliminarily assessed on two simple simulation examples, where we show the benefits of using nonlinear local models and artificially generated unsupervised data. Copyright (C) 2021 The Authors.
引用
收藏
页码：421 / 426
页数：6
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