Open AccessResearch on the influence of hidden layers on the prediction accuracy of GA-BP neural network
Author(s) -
Yacheng Xing,
Fanfei Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1486/2/022010
Subject(s) - artificial neural network , computer science , artificial intelligence , pattern recognition (psychology) , time delay neural network , algorithm
In order to improve the prediction accuracy of GA-BP neural network, the effect of hidden layer neurons number on the prediction accuracy of algorithm was considered and an improved GA-BP neural network was proposed. Taking 60 equipment maintenance time of a photovoltaic charging station as the specimens, the high prediction accuracy of improved GA-BP neural network has been proved. Results indicate that the GA-BP neural network has the highest prediction accuracy when the number of hidden layer neurons is 5. The average relative error of improved GA-BP neural network between the predicted values and the expected values is 6.1%, decreasing 57% compared with BP neural network. The prediction accuracy of improved GA-BP neural network is much higher than that of BP neural network, and the predicted time can provide a basis for personnel scheduling.