Learning the 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 variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations (SDEs), based on which the loss function is built. The stochastic gradient descent 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 the parameters of a class of stochastic Lotka-Volterra systems with neural networks[J]. Journal of University of Chinese Academy of Sciences, 2025, 42(1): 20-25.

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