scholarly journals On the complexity of piecewise affine system identification

Automatica ◽  
2015 ◽  
Vol 62 ◽  
pp. 148-153 ◽  
Author(s):  
Fabien Lauer
Keyword(s):  
System Identification ◽  
Piecewise Affine ◽  
Affine System ◽  
Piecewise Affine System

A bounded-error approach to piecewise affine system identification

2005 ◽  
Vol 50 (10) ◽  
pp. 1567-1580 ◽  
Author(s):  
A. Bempora ◽  
A. Garulli ◽  
S. Paoletti ◽  
A. Vicino
Keyword(s):  
System Identification ◽  
Piecewise Affine ◽  
Bounded Error ◽  
Affine System ◽  
Piecewise Affine System

An output error algorithm for piecewise affine system identification

2012 ◽  
Vol 20 (4) ◽  
pp. 444-452 ◽  
Author(s):  
Niel Canty ◽  
Thomas O'Mahony ◽  
Marcin T. Cychowski
Keyword(s):  
System Identification ◽  
Piecewise Affine ◽  
Output Error ◽  
Affine System ◽  
Piecewise Affine System

A survey on switched and piecewise affine system identification

2012 ◽  
Vol 45 (16) ◽  
pp. 344-355 ◽  
Author(s):  
Andrea Garulli ◽  
Simone Paoletti ◽  
Antonio Vicino
Keyword(s):  
System Identification ◽  
Piecewise Affine ◽  
Affine System ◽  
Piecewise Affine System

A bilevel programming framework for piecewise affine system identification

2019 ◽  
Author(s):  
Simone Paoletti ◽  
Iacopo Savelli ◽  
Andrea Garulli ◽  
Antonio Vicino
Keyword(s):  
System Identification ◽  
Bilevel Programming ◽  
Piecewise Affine ◽  
Programming Framework ◽  
Affine System ◽  
Piecewise Affine System

scholarly journals Statistical Inference for Piecewise Affine System Identification

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hong Jianwang ◽  
Ricardo A. Ramirez-Mendoza ◽  
Xiang Yan
Keyword(s):  
System Identification ◽  
Unknown Parameter ◽  
Recursive Least Squares ◽  
Parameter Vector ◽  
Input Output ◽  
Output Data ◽  
Piecewise Affine ◽  
Affine System ◽  
Piecewise Affine System ◽  
Unknown Parameter Vector

This short note studies the problem of piecewise affine system identification, being a special nonlinear system based on our previous contribution on it. Two different identification strategies are proposed to achieve our mission, such as centralized identification and distributed identification. More specifically, for centralized identification, the total observed input-output data are used to estimate all unknown parameter vectors simultaneously without any consideration on the classification process. But for distributed identification, after the whole observed input-output data are classified into their own right subregions, then part input-output data, belonging to the same subregion, are applied to estimate the unknown parameter vector. Whatever the centralized identification and distributed identification, the final decision is to determine the unknown parameter vector in one linear form, so the recursive least squares algorithm and its modified form with the dead zone are studied to deal with the statistical noise and bounded noise, respectively. Finally, one simulation example is used to compare the identification accuracy for our considered two identification strategies.

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