System Identification Algorithm for Non-Uniformly Sampled Data

Korkut Bekiroglu, Constantino Lagoa, Stephanie T. Lanza, Mario Sznaier

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Considerable effort has been devoted to the development of algorithms for identification of parsimonious discrete time models from noisy input/output data sets since this facilitates controller design. Several methods, such as nuclear norm minimization, have been used to provide approximate solutions to this non-convex problem. However, even though the field of continuous time system identification is now mature, results on parsimonious model identification of continuous time systems are still very limited. In this paper, an atomic norm minimization method is proposed for this purpose that can handle non-uniformly sampled data without preprocessing. The proposed approach provides an efficient way to use noisy, non-uniformly sampled data to determine a reliable, low-order continuous time model. Numerical performance is illustrated using academic examples and simulated behavioral data from a smoking cessation study.

Original languageEnglish (US)
Pages (from-to)7296-7301
Number of pages6
JournalIFAC-PapersOnLine
Volume50
Issue number1
DOIs
StatePublished - Jul 2017

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'System Identification Algorithm for Non-Uniformly Sampled Data'. Together they form a unique fingerprint.

  • Cite this