Demand Forecast on the Orchard Cold Chain Logistics in Hunan Province Based on Cognitive Neuroscience
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Published:2022-01-14
Issue:
Volume:16
Page:571-577
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ISSN:1998-4464
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Container-title:International Journal of Circuits, Systems and Signal Processing
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language:en
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Short-container-title:
Author:
Fu Ying1, Zeng Xiangpeng2, Li Yihua1, Wen Yiming3, Wen Xiaowei4
Affiliation:
1. College of Logistics and Transportation, Central South University of Forestry and Technology, Changsha 410004, China 2. School of Medical Humanities and management, Hunan Medical College, Huaihua 418000, China 3. School of Mathematics, University of Birmingham, B15 2TT, United Kingdom 4. Department of Agricultural and Business Administration, South China Agricultural University, Guangzhou 510642, China
Abstract
How to scientifically and effectively predict the cold chain logistics demand and provide basis for decision making has always been the focus of forestry and orchard logistics research. From the learning environment of neurons, cognitive neuroscience provides a new perspective for forecasting the demand for cold chain logistics. This paper uses the cognitive neuroscience theory to construct a BP neural network model containing two hidden layers to predict the cold chain logistics demand of the forestry and orchard industry in Hunan province in 2017-2021. Suggestions are then given from the aspects of cold chain logistics construction, transportation infrastructure construction, government policy, enterprise and industry according to the prediction results, thus, providing a theoretical basis for the planning of the cold chain logistics system of Hunan province in a certain period of time, as well as references for the development of cold chain logistics in other parts of the country.
Publisher
North Atlantic University Union (NAUN)
Subject
Electrical and Electronic Engineering,Signal Processing
Reference26 articles.
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