Learning Parameters of a Class of Stochastic Lotka-Volterra Systems with Neural Networks*

doi:10.7523/j.ucas.2023.012

Abstract

Abstract: In this paper, we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems. Approximations of the mean and covariance matrix of the observational variable are obtained from the Euler-Maruyama discretization of the underlying SDE, based on which the loss function is built. Stochastic gradient decent method is applied in the neural network training. Numerical experiments demonstrate the effectiveness of our method.

Key words: stochastic Lotka-Volterra systems, neural networks, Euler-Maruyama scheme, parameter estimation

CLC Number:

O241.8

WANG Zhanpeng, WANG Lijin. Learning Parameters of a Class of Stochastic Lotka-Volterra Systems with Neural Networks^*[J]. Journal of University of Chinese Academy of Sciences, DOI: 10.7523/j.ucas.2023.012.

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Learning Parameters of a Class of Stochastic Lotka-Volterra Systems with Neural Networks^*

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