Research on library visits prediction based on the combination of GM (1,1) and BP neural network

Leilei Peng; Ying Liu; Ke Chen

doi:10.1117/12.2628694

7 March 2022 Research on library visits prediction based on the combination of GM (1,1) and BP neural network

Leilei Peng, Ying Liu, Ke Chen

Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 121671B (2022) https://doi.org/10.1117/12.2628694
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China

Abstract

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.

Citation Download Citation

Leilei Peng, Ying Liu, and Ke Chen "Research on library visits prediction based on the combination of GM (1,1) and BP neural network", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 121671B (7 March 2022); https://doi.org/10.1117/12.2628694

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