Matrix rank minimization approach to signal recovery and nonlinear function estimation for nonlinear ARX model with input nonlinearity

Katsumi Konishi, Masashi Fujii, Katsuyuki Kunida, Shinsuke Uda, Shinya Kuroda

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

Abstract

This paper deals with an input/output signal recovery problem for nonlinear multiple-input single-output autoregressive exogenous (ARX) models with input nonlinearity, which are used in data-driven systems biology. A matrix rank minimization approach is applied, and a new signal recovery algorithm for nonlinear ARX models is provided. The proposed algorithm recovers output signals and nonlinear-transformed input signals on a linear subspace using some assumptions about nonlinear functions and does not require the exact knowledge of nonlinear functions. Numerical examples using experimental data of signal transduction of cellular systems show the efficiency of the proposed algorithm.

Original languageEnglish
Title of host publication2017 Asian Control Conference, ASCC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1428-1431
Number of pages4
ISBN (Electronic)9781509015733
DOIs
Publication statusPublished - Feb 7 2018
Event2017 11th Asian Control Conference, ASCC 2017 - Gold Coast, Australia
Duration: Dec 17 2017Dec 20 2017

Publication series

Name2017 Asian Control Conference, ASCC 2017
Volume2018-January

Other

Other2017 11th Asian Control Conference, ASCC 2017
CountryAustralia
CityGold Coast
Period12/17/1712/20/17

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All Science Journal Classification (ASJC) codes

  • Control and Optimization

Cite this

Konishi, K., Fujii, M., Kunida, K., Uda, S., & Kuroda, S. (2018). Matrix rank minimization approach to signal recovery and nonlinear function estimation for nonlinear ARX model with input nonlinearity. In 2017 Asian Control Conference, ASCC 2017 (pp. 1428-1431). (2017 Asian Control Conference, ASCC 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASCC.2017.8287382