EM-based identification of sparse FIR systems having quantized data

Title: EM-based identification of sparse FIR systems having quantized data
Creator: Carvajal, Rodrigo; Agüero, Juan C.; Godoy, Boris I.; Goodwin, Graham C.; Yuz, Juan I.
Relation: 16th IFAC Symposium on System Identification. IFAC Proceedings Volumes, Volume 45, Issue 16, July 2012 (Brussels, Belgium 11-13 July, 2012) p. 553-558
Publisher Link: http://dx.doi.org/10.3182/20120711-3-BE-2027.00253
Publisher: International Federation of Automatic Control (IFAC)
Resource Type: conference paper
Date: 2012
Description: 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.
Subject: quantized estimation; EM algorithm; sparse optimization; regularization
Identifier: http://hdl.handle.net/1959.13/1326320
Identifier: uon:25399
Identifier: ISBN:9783902823069
Language: eng
Reviewed

		Thumbnail	File	Description	Size	Format