Imperial College London


Faculty of EngineeringDepartment of Chemical Engineering

Reader in Process Systems Engineering



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BibTex format

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 = {},
volume = {70},
year = {2018}

RIS format (EndNote, RefMan)

AB - This paper introducesset-membership nonlinear regression(SMR), a new approach to nonlinearregression under uncertainty. The problem is to determine the subregion in parameter spaceenclosing all (global) solutions to a nonlinear regression problem in the presence of boundeduncertainty on the observed variables. Our focus is on nonlinear algebraic models. We investigatethe 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 boundedmeasurement errors. We also develop a computational framework to describe tight enclosures ofthe SMR regions using semi-infinite programming and complete-search methods, in the form oflikelihood contour and polyhedral enclosures. The case study of a parameter estimation problemin microbial growth is presented to illustrate various theoretical and computational aspects of theSMR 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 -
UR -
UR -
VL - 70
ER -