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

doi:10.1109/ASCC.2017.8287382

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 proceeding › Conference 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 language	English
Title of host publication	2017 Asian Control Conference, ASCC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1428-1431
Number of pages	4
ISBN (Electronic)	9781509015733
DOIs	https://doi.org/10.1109/ASCC.2017.8287382
Publication status	Published - 07-02-2018
Externally published	Yes
Event	2017 11th Asian Control Conference, ASCC 2017 - Gold Coast, Australia Duration: 17-12-2017 → 20-12-2017

Publication series

Name	2017 Asian Control Conference, ASCC 2017
Volume	2018-January

Conference

Conference	2017 11th Asian Control Conference, ASCC 2017
Country/Territory	Australia
City	Gold Coast
Period	17-12-17 → 20-12-17

All Science Journal Classification (ASJC) codes

Control and Optimization

Access to Document

10.1109/ASCC.2017.8287382

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

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

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.",

author = "Katsumi Konishi and Masashi Fujii and Katsuyuki Kunida and Shinsuke Uda and Shinya Kuroda",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 11th Asian Control Conference, ASCC 2017 ; Conference date: 17-12-2017 Through 20-12-2017",

year = "2018",

month = feb,

day = "7",

doi = "10.1109/ASCC.2017.8287382",

language = "English",

series = "2017 Asian Control Conference, ASCC 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2017 Asian Control Conference, ASCC 2017",

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}

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. 2017 Asian Control Conference, ASCC 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1428-1431, 2017 11th Asian Control Conference, ASCC 2017, Gold Coast, Australia, 17-12-17. https://doi.org/10.1109/ASCC.2017.8287382

Matrix rank minimization approach to signal recovery and nonlinear function estimation for nonlinear ARX model with input nonlinearity. / Konishi, Katsumi; Fujii, Masashi; Kunida, Katsuyuki et al.
2017 Asian Control Conference, ASCC 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1428-1431 (2017 Asian Control Conference, ASCC 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Matrix rank minimization approach to signal recovery and nonlinear function estimation for nonlinear ARX model with input nonlinearity

AU - Konishi, Katsumi

AU - Fujii, Masashi

AU - Kunida, Katsuyuki

AU - Uda, Shinsuke

AU - Kuroda, Shinya

PY - 2018/2/7

Y1 - 2018/2/7

N2 - 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.

AB - 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.

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M3 - Conference contribution

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T3 - 2017 Asian Control Conference, ASCC 2017

SP - 1428

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BT - 2017 Asian Control Conference, ASCC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 11th Asian Control Conference, ASCC 2017

Y2 - 17 December 2017 through 20 December 2017

ER -

Konishi K, Fujii M, Kunida K, Uda S, Kuroda S. 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. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1428-1431. (2017 Asian Control Conference, ASCC 2017). doi: 10.1109/ASCC.2017.8287382

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

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