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 | |
| 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.Keywords
- Ordinary Differential Equations
- Particle Swarm Optimization
- Latin Hypercube Sampling
- Structure and parameters optimization
Access to Document
- 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