Open AccessResearch of Neural Network Structural Optimization Based on Information Entropy
Author(s) -
WANG Danyang,
SHAO Fangming
Publication year - 2020
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2020.05.006
Subject(s) - computer science , artificial neural network , artificial intelligence , entropy (arrow of time) , heuristic , machine learning , stochastic neural network , decision tree , tree structure , network structure , pattern recognition (psychology) , time delay neural network , algorithm , physics , quantum mechanics , binary tree
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.