Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization

被引：8

作者：

Akman D. ^{[1
]}

Akman O. ^{[2
]}

Schaefer E. ^{[3
]}

机构：

[1] University of Illinois Urbana-Champaign, United States

[2] Illinois State University, United States

[3] Marymount University, United States

来源：

Journal of Applied Mathematics | 2018年 / 2018卷

关键词：

Particle swarm optimization (PSO);

D O I：

10.1155/2018/9160793

中图分类号：

学科分类号：

摘要：

Researchers using ordinary differential equations to model phenomena face two main challenges among others: implementing the appropriate model and optimizing the parameters of the selected model. The latter often proves difficult or computationally expensive. Here, we implement Particle Swarm Optimization, which draws inspiration from the optimizing behavior of insect swarms in nature, as it is a simple and efficient method for fitting models to data. We demonstrate its efficacy by showing that it outstrips evolutionary computing methods previously used to analyze an epidemic model. © 2018 Devin Akman et al.

引用

共 19 条

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