LatinPSO: An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models

Xinliang Tian; Wei Pang; Yizhang Wang; Kaimin Guo; You Zhou

doi:10.1016/j.biosystems.2019.05.006

LatinPSO: An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models

Xinliang Tian
, Wei Pang
, Yizhang Wang
, Kaimin Guo
, You Zhou (Corresponding Author)

Jilin University
Key Laboratory of Symbolic Computation and Knowledge, Engineering of Ministry of Education

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

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Abstract

Simultaneously inferring both the structure and parameters of Ordinary Differential Equations (ODEs) for a complex dynamic system is more practical in many systems identification problems, but it remains challenging due to the complexity of the underlying search space. In this research, we propose a novel algorithm based on Particle Swarm Optimization (PSO) and Latin Hypercube Sampling (LHS) to address the above problem. The proposed algorithm is termed LatinPSO, and it can be effectively used for inferring the structure and parameters of ODE models through time course data. To start with, the real Human Immunodeficiency Virus (HIV) model and several synthetic models are used for evaluating the performance of LatinPSO. Experimental results demonstrated that LatinPSO could find satisfactory candidate ODE models with appropriate structure and parameters.

Original language	English
Pages (from-to)	8-16
Number of pages	9
Journal	BioSystems
Volume	182
Early online date	2 Jun 2019
DOIs	https://doi.org/10.1016/j.biosystems.2019.05.006
Publication status	Published - Aug 2019

Bibliographical note

This research is supported by the National Natural Science Foundation of China (Grants Nos.61772227, 61572227), the Science & Technology Development Foundation of Jilin Province (Grants No. 20180201045GX), the Science Foundation of Education Department of Guangdong Province (Grants Nos. 2017KQNCX251, 2018XJCQSQ026) and the Social Science Foundation of Education Department of Jilin Province (Grants No. JJKH20181315SK). WP was supported by the 2015 Scottish Crucible award funded by Royal Society of Edinburgh.

Funding

This research is supported by the National Natural Science Foundation of China (Grants Nos. 61772227 , 61572227 ), the Science & Technology Development Foundation of Jilin Province (Grants No. 20180201045GX ), the Science Foundation of Education Department of Guangdong Province (Grants Nos. 2017KQNCX251 , 2018XJCQSQ026 ) and the Social Science Foundation of Education Department of Jilin Province (Grants No. JJKH20181315SK ). WP was supported by the 2015 Scottish Crucible award funded by Royal Society of Edinburgh .

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Ordinary Differential Equations
Particle Swarm Optimization
Latin Hypercube Sampling
Structure and parameters optimization

Access to Document

10.1016/j.biosystems.2019.05.006Licence: Unspecified

Accepted Manuscript
© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0
Accepted author manuscript, 382 KBLicence: CC BY-NC-ND

Cite this

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title = "LatinPSO: An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models ",

abstract = "Simultaneously inferring both the structure and parameters of Ordinary Differential Equations (ODEs) for a complex dynamic system is more practical in many systems identification problems, but it remains challenging due to the complexity of the underlying search space. In this research, we propose a novel algorithm based on Particle Swarm Optimization (PSO) and Latin Hypercube Sampling (LHS) to address the above problem. The proposed algorithm is termed LatinPSO, and it can be effectively used for inferring the structure and parameters of ODE models through time course data. To start with, the real Human Immunodeficiency Virus (HIV) model and several synthetic models are used for evaluating the performance of LatinPSO. Experimental results demonstrated that LatinPSO could find satisfactory candidate ODE models with appropriate structure and parameters.",

keywords = "Ordinary Differential Equations, Particle Swarm Optimization, Latin Hypercube Sampling, Structure and parameters optimization",

author = "Xinliang Tian and Wei Pang and Yizhang Wang and Kaimin Guo and You Zhou",

note = "This research is supported by the National Natural Science Foundation of China (Grants Nos.61772227, 61572227), the Science \& Technology Development Foundation of Jilin Province (Grants No. 20180201045GX), the Science Foundation of Education Department of Guangdong Province (Grants Nos. 2017KQNCX251, 2018XJCQSQ026) and the Social Science Foundation of Education Department of Jilin Province (Grants No. JJKH20181315SK). WP was supported by the 2015 Scottish Crucible award funded by Royal Society of Edinburgh.",

year = "2019",

month = aug,

doi = "10.1016/j.biosystems.2019.05.006",

language = "English",

volume = "182",

pages = "8--16",

journal = "BioSystems",

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AU - Tian, Xinliang

AU - Pang, Wei

AU - Wang, Yizhang

AU - Guo, Kaimin

AU - Zhou, You

N1 - This research is supported by the National Natural Science Foundation of China (Grants Nos.61772227, 61572227), the Science & Technology Development Foundation of Jilin Province (Grants No. 20180201045GX), the Science Foundation of Education Department of Guangdong Province (Grants Nos. 2017KQNCX251, 2018XJCQSQ026) and the Social Science Foundation of Education Department of Jilin Province (Grants No. JJKH20181315SK). WP was supported by the 2015 Scottish Crucible award funded by Royal Society of Edinburgh.

PY - 2019/8

Y1 - 2019/8

N2 - Simultaneously inferring both the structure and parameters of Ordinary Differential Equations (ODEs) for a complex dynamic system is more practical in many systems identification problems, but it remains challenging due to the complexity of the underlying search space. In this research, we propose a novel algorithm based on Particle Swarm Optimization (PSO) and Latin Hypercube Sampling (LHS) to address the above problem. The proposed algorithm is termed LatinPSO, and it can be effectively used for inferring the structure and parameters of ODE models through time course data. To start with, the real Human Immunodeficiency Virus (HIV) model and several synthetic models are used for evaluating the performance of LatinPSO. Experimental results demonstrated that LatinPSO could find satisfactory candidate ODE models with appropriate structure and parameters.

AB - Simultaneously inferring both the structure and parameters of Ordinary Differential Equations (ODEs) for a complex dynamic system is more practical in many systems identification problems, but it remains challenging due to the complexity of the underlying search space. In this research, we propose a novel algorithm based on Particle Swarm Optimization (PSO) and Latin Hypercube Sampling (LHS) to address the above problem. The proposed algorithm is termed LatinPSO, and it can be effectively used for inferring the structure and parameters of ODE models through time course data. To start with, the real Human Immunodeficiency Virus (HIV) model and several synthetic models are used for evaluating the performance of LatinPSO. Experimental results demonstrated that LatinPSO could find satisfactory candidate ODE models with appropriate structure and parameters.

KW - Ordinary Differential Equations

KW - Particle Swarm Optimization

KW - Latin Hypercube Sampling

KW - Structure and parameters optimization

UR - http://www.mendeley.com/research/latinpso-algorithm-simultaneously-inferring-structure-parameters-ordinary-differential-equations-mod

U2 - 10.1016/j.biosystems.2019.05.006

DO - 10.1016/j.biosystems.2019.05.006

M3 - Article

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SN - 0303-2647

VL - 182

SP - 8

EP - 16

JO - BioSystems

JF - BioSystems

ER -

LatinPSO: An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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