EM-based identification of sparse FIR systems having quantized data

Rodrigo Carvajal, Juan C. Agüero, Boris I. Godoy, Graham C. Goodwin, Juan I. Yuz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


In this paper, we explore the identification of sparse FIR systems having quantized output data. Our approach is based on the use of regularization. We explore several aspects concerning the implementation of the Expectation-Maximization (EM) algorithm, including: i) a general framework, based on mean-variance Gaussian mixtures, for incorporating a regularization term that forces sparsity, ii) utilization of Markov Chain Monte Carlo techniques (namely a Gibbs sampler) and scenarios to implement the EM algorithm for multiple input multiple output systems. We show that for single input single output systems, it is possible to obtain closed form expressions for solving the EM algorithm.

Original languageEnglish
Title of host publicationSYSID 2012 - 16th IFAC Symposium on System Identification, Final Program
PublisherIFAC Secretariat
Number of pages6
EditionPART 1
ISBN (Print)9783902823069
StatePublished - 2012
Externally publishedYes
EventUniversite Libre de Bruxelles - Bruxelles, Belgium
Duration: 11 Jul 201213 Jul 2012

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
NumberPART 1
ISSN (Print)1474-6670


ConferenceUniversite Libre de Bruxelles


  • EM algorithm
  • Quantized estimation
  • Regularization
  • Sparse optimization


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