摘要
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.
提出一种利用神经网络学习一类随机Lotka-Volterra系统参数的方法。利用随机微分方程的Euler-Maruyama离散近似推导出观测变量的期望和协方差矩阵,并在此基础上建立损失函数。在网络训练过程中,采用随机梯度下降方法进行优化。数值实验验证了该算法的有效性。
出处
《中国科学院大学学报(中英文)》
北大核心
2025年第1期20-25,共6页
Journal of University of Chinese Academy of Sciences
基金
Supported by the National Natural Science Foundation of China(11971458,11471310)。
