Research on Library Visits Prediction based on the Combination of GM (1,1) and BP Neural Network

被引：0
|
作者：
Peng, Leilei [1 ]
Liu, Ying [1 ]
Chen, Ke [1 ]
机构：
[1] Sichuan Univ, Lib, Chengdu 610065, Sichuan, Peoples R China
来源：
THIRD INTERNATIONAL CONFERENCE ON ELECTRONICS AND COMMUNICATION; NETWORK AND COMPUTER TECHNOLOGY (ECNCT 2021) | 2022年 / 12167卷
关键词：
Library Visits; GM (1,1); BP Neural Network; PUBLIC-LIBRARIES;
D O I：
10.1117/12.2628694
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
All work of library is carried out around the readers, library visits prediction is crucial to the allocation and optimization of library human, financial, and material resources. Based on the construction of GM (1,1) model and the Back Propagation Neural Network (BPNN) model, this study established a combination model of GM (1,1)-BPNN, and takes Jiangan Library of Sichuan University as the case study, and fits the 36-month data from January 2017 to December 2019 for model verification. Results show that, in terms of library visit prediction, GM (1,1)- BPNN has smaller errors, higher prediction accuracy, and better stability than GM (1,1) or BPNN.
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页数：7
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