Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization
| Autor: | Elsa Schaefer, Devin Akman, Olcay Akman |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
021103 operations research Article Subject Estimation theory Computer science lcsh:Mathematics Applied Mathematics 0211 other engineering and technologies Particle swarm optimization 02 engineering and technology lcsh:QA1-939 Evolutionary computation Simple (abstract algebra) Ordinary differential equation Face (geometry) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Epidemic model |
| Zdroj: | Journal of Applied Mathematics, Vol 2018 (2018) J. Appl. Math. |
| ISSN: | 1687-0042 1110-757X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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