Multi-parameter Grain Analysis Model based on BP Neural Network

DOI

10.2991/tlicsc-18.2018.62How to use a DOI?

Keywords

Neural Networks; Grain situation analysis; Multi-parameter grain situation.

Abstract

During the process of grain storage, changes in seasons and weather will cause changes in the temperature and humidity of the granary, which will affect the occurrence of pests in the grain pile and the possibility of condensation, leading to problems such as grain deterioration and water loss. In this paper, based on the Bp neural network model, parameters such as temperature, humidity, moisture, warehouse temperature and warehouse moisture are used as input factors of the model to construct a multi-parameter grain analysis model. Through comparative analysis, the model optimization parameters are selected, and the trained neural network model is convenient for food administrators to obtain food storage status more intuitively.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yufeng Zhuang
AU  - Runfa Lu
PY  - 2018/12
DA  - 2018/12
TI  - Multi-parameter Grain Analysis Model based on BP Neural Network
BT  - Proceedings of the 2018 International Conference on Transportation & Logistics, Information & Communication, Smart City (TLICSC 2018)
PB  - Atlantis Press
SP  - 387
EP  - 392
SN  - 1951-6851
UR  - https://doi.org/10.2991/tlicsc-18.2018.62
DO  - 10.2991/tlicsc-18.2018.62
ID  - Zhuang2018/12
ER  -