Convex relaxations for robust identification of Wiener systems and applications

Burak Yilmaz, Mustafa Ayazoglu, Mario Sznaier, Constantino Lagoa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

This paper considers the identification of Wiener systems in a worst case framework. Given some a priori information about the admissible set of plants, nonlinearities and measurement noise, and a posteriori experimental data, our goal is twofold: (i) establish whether the a priori and a posteriori information are consistent, and (ii) in that case find a model that interpolates the available experimental information within the noise level. As recently shown, this problem is generically NP hard both in the number of data points and the number of inputs to the non-linearity. Our main result shows that a computationally attractive relaxation can be obtained by recasting the problem as a rank-constrained semi-definite optimization and using existing tools specifically tailored to this type of problems. These results are illustrated with a practical application drawn from computer vision

Original languageEnglish (US)
Title of host publication2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2812-2818
Number of pages7
ISBN (Print)9781612848006
DOIs
StatePublished - 2011
Event2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 - Orlando, FL, United States
Duration: Dec 12 2011Dec 15 2011

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Other

Other2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
Country/TerritoryUnited States
CityOrlando, FL
Period12/12/1112/15/11

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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