Nonlinear system identification using compressed sensing

Manjish Naik, Douglas Cochran

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

5 Scopus citations


This paper describes an approach to system identification based on compressive sensing and demonstrates its efficacy on a challenging classical benchmark single-input, multiple output (SIMO) mechanical system consisting of an inverted pendulum on a cart. The differential equations describing the system dynamics are to be determined from measurements of the system's input-output behavior. These equations are assumed to consist of the superposition, with unknown weights, of a small number of terms drawn from a large library of nonlinear terms. Under this assumption, compressed sensing allows the constituent library elements and their corresponding weights to be identified by decomposing a time-series signal of the system's outputs into a sparse superposition of corresponding time-series signals produced by the library components.

Original languageEnglish (US)
Title of host publicationConference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Number of pages5
StatePublished - 2012
Event46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012 - Pacific Grove, CA, United States
Duration: Nov 4 2012Nov 7 2012

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Other46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Country/TerritoryUnited States
CityPacific Grove, CA


  • Basis Pursuit
  • Compressed Sensing
  • Inverted Pendulum
  • Non-Linear
  • Sparsity
  • System Identification

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications


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