TY - JOUR
T1 - LatinPSO
T2 - An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models
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
AN - SCOPUS:85066958443
VL - 182
SP - 8
EP - 16
JO - BioSystems
JF - BioSystems
SN - 0303-2647
ER -
