Imperial College London
Alternate

ProfessorBenoitChachuat

Faculty of Engineering, Department of Chemical Engineering

Professor of Process Systems Engineering
 
 
 
//

Contact

 

b.chachuat Website

 
 
//

Location

 

609Roderic Hill BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Peric:2018:10.1016/j.jprocont.2018.04.002,
author = {Peric, N and Paulen, R and Villanueva, ME and Chachuat, B},
doi = {10.1016/j.jprocont.2018.04.002},
journal = {Journal of Process Control},
pages = {80--95},
title = {Set-membership nonlinear regression approach to parameter estimation},
url = {http://dx.doi.org/10.1016/j.jprocont.2018.04.002},
volume = {70},
year = {2018}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This paper introduces set-membership nonlinear regression (SMR), a new approach to nonlinear regression under uncertainty. The problem is to determine the subregion in parameter space enclosing all (global) solutions to a nonlinear regression problem in the presence of bounded uncertainty on the observed variables. Our focus is on nonlinear algebraic models. We investigate the connections of SMR with (i) the classical statistical inference methods, and (ii) the usual set-membership estimation approach where the model predictions are constrained within bounded measurement errors. We also develop a computational framework to describe tight enclosures of the SMR regions using semi-infinite programming and complete-search methods, in the form of likelihood contour and polyhedral enclosures. The case study of a parameter estimation problem in microbial growth is presented to illustrate various theoretical and computational aspects of the SMR approach.
AU  - Peric,N
AU  - Paulen,R
AU  - Villanueva,ME
AU  - Chachuat,B
DO  - 10.1016/j.jprocont.2018.04.002
EP  - 95
PY  - 2018///
SN  - 0959-1524
SP  - 80
TI  - Set-membership nonlinear regression approach to parameter estimation
T2  - Journal of Process Control
UR  - http://dx.doi.org/10.1016/j.jprocont.2018.04.002
UR  - https://www.sciencedirect.com/science/article/pii/S0959152418300647?via=ihub
UR  - http://hdl.handle.net/10044/1/58980
VL  - 70
ER  -
Download