Controlled complete ARMA Independent Process Analysis

Zoltán Szabó, András Lorincz

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

2 Citations (Scopus)

Abstract

In this paper we address the controlled complete AutoRegressive Moving Average Independent Process Analysis (ARMAX-IPA; X-exogenous input or control) problem, which is a generalization of the Blind SubSpace Deconvolution (BSSD) task. Compared to our previous work that dealt with the undercomplete situation, (i) here we extend the theory to complete systems, (ii) allow an autoregressive part to be present, (iii) and include exogenous control. We investigate the case when the observed signal is a linear mixture of independent multidimensional ARMA processes that can be controlled. Our objective is to estimate the ARMA processes, their driving noises as well as the mixing. We aim efficient estimation by choosing suitable control values. For the optimal choice of the control we adapt the D-optimality principle, also known as the 'InfoMax method'. We solve the problem by reducing it to a fully observable D-optimal ARX task and Independent Subspace Analysis (ISA) that we can solve. Numerical examples illustrate the efficiency of the proposed method.

Original languageEnglish
Title of host publication2009 International Joint Conference on Neural Networks, IJCNN 2009
Pages3038-3045
Number of pages8
DOIs
Publication statusPublished - 2009
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
Duration: Jun 14 2009Jun 19 2009

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2009 International Joint Conference on Neural Networks, IJCNN 2009
CountryUnited States
CityAtlanta, GA
Period6/14/096/19/09

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Szabó, Z., & Lorincz, A. (2009). Controlled complete ARMA Independent Process Analysis. In 2009 International Joint Conference on Neural Networks, IJCNN 2009 (pp. 3038-3045). [5178797] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2009.5178797