Research on Gaussian-wavelet-type Activation Function of Neural Network Hidden Layer Based on Monte Carlo Method

Artificial neural networks have developed rapidly in recent years and have been applied in the fields of image recognition, natural language processing, and pattern recognition. The activation function, as an integral part of the neural network, plays a huge role in the neural network. The appropriateness of the activation function determines the accuracy of the neural network results. In this paper, a Monte Carlo method combined with the Gaussian-wavelet-type activation function to design a neural network and apply it to the image classification of convolutional neural networks. The Gaussian-wavelet-type activation function and the Monte Carlo method are combined to select the most suitable activation function to ensure the stability of the whole training and improve the accuracy of the classification results on the data set.