Neural network calculation method for nonlinear singular perturbation differential equation

The invention discloses a neural network calculation method for a nonlinear singular perturbation differential equation. The method comprises the following steps: initializing hyper-parameters of truncated logarithmic normal distribution for training sample point generation; initializing the number of hidden layers in the neural network, the number of corresponding neurons in the ith hidden layer and an activation function; initializing weight matrix parameters and bias vector parameters of all hidden layers in the neural network; determining an optimization algorithm and a learning rate; sampling from the truncated logarithmic normal distribution to obtain an input sample point set; and constructing an approximate solution of a singular perturbation problem through a neural network, and substituting the approximate solution into the differential equation to obtain a loss function. According to the embodiment of the invention, aiming at the boundary layer characteristics of a nonlinear singular perturbation e