Research of Neural Network Structural Optimization Based on Information Entropy

Abstract

In the application of deep learning, the depth and width of the neural network structure have a great influence on the learning performance of the neural network. This paper focuses on structural optimization of depth and width, leveraging the information entropy model and decision tree strategy as feature selection and structural adjustment to optimize neural network candidates. Therefore, a decision tree-based heuristic optimization algorithm for neural network structural adjustment is proposed. Furthermore, the proposed approach is applied to fully-connected neural networks trained on the Iris dataset, and the proposed approach is verified effective via experimental simulation.