Abstract
This paper presents an evolutionary method for identifying a system of ordinary differential equations (ODEs) from the observed time series data. The structure of ODE is inferred by the Multi Expression Programming (MEP) and the ODE’s parameters are optimized by using particle swarm optimization (PSO). The experimental results on chemical reaction modeling problems show effectiveness of the proposed method.
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Yang, B., Chen, Y., Meng, Q. (2009). Inference of Differential Equations for Modeling Chemical Reactions. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_114
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DOI: https://doi.org/10.1007/978-3-642-01507-6_114
Publisher Name: Springer, Berlin, Heidelberg
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