Forecasting Regional Logistics Demand of Agricultural Products Logistics Park in YuHang County Based on Artificial Neural Network

ICLEM 2010 ◽  
2010 ◽  
Author(s):  
Youli Xu ◽  
Ruiyu Pan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Agricultural Products ◽  
Logistics Park ◽  
Regional Logistics ◽  
Artificial Neural ◽  
Logistics Demand

An Integrated Predicting Model of New-Built Regional Logistics Center’s Demand Based on the Artificial Neural Network

2012 ◽  
Vol 253-255 ◽  
pp. 1512-1517
Author(s):  
Jian Feng Luo ◽  
Tian Shan Ma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Factor Analysis ◽  
Back Propagation ◽  
Demand Forecast ◽  
Logistics Center ◽  
Bp Artificial Neural Network ◽  
Regional Logistics ◽  
Artificial Neural ◽  
Logistics Demand

In order to predict the scale of logistics demand for a new-built regional center, economic indicators and the other related measuring indicator of the scale for logistics demand is studied. The factor analysis and back propagation (BP) artificial neural network theory are applied to set up a model for predicting the scale of the logistics center’ s demand. The factor analysis is applied to this model to reduce the number of indicators of the input layer in the BP artificial neural network, and to reduce complexity. Then model is introduced to fit historical data of the scale of new –built a regional logistics center’ s demand .Finally,a third-layer BP artificial neural network is constructed. This model was applied to predict the scale of the logistics demand in an example and the forecasting result shows that forecasting accuracy of the model is good. It also provides a new way of a new-built regional logistics center’ s demand forecast.

scholarly journals Parasite Analyser using Artificial Neural Network

2019 ◽  
Vol 9 (2) ◽  
pp. 5415-5418
Keyword(s):  
Neural Network ◽  
At Risk ◽  
Artificial Neural Network ◽  
Control Measure ◽  
Agricultural Products ◽  
Parasite Population ◽  
Economic Threshold ◽  
Network Method ◽  
Artificial Neural ◽  
Artificial Network

Crop destruction causes so much damage to the crops or agricultural products which causes reduction in the productivity. There are numerous important crops are at risk. Parasites are controlled environmentally by unfriendly pesticides. Once a pest has reached either an economic threshold, or intolerable level action should be taken. Pesticides are used as a control measure while other strategies will not bring the parasite population under the threshold. So, here we use early parasite analyser to detect the parasite in a plant and its location. We use artificial network method to analyse the parasite. ANN technique is used to detect the pest in the field. This method is used to resolve the problem of classification identification, authentication, diagnostics, optimization and approximation.

scholarly journals Inventory Management of Railway Logistics Park Based on Artificial Neural Network

2020 ◽  
Vol 53 (5) ◽  
pp. 715-723
Author(s):  
Li Gao ◽  
Huandi Dou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inventory Management ◽  
Logistics Park ◽  
Factors Affecting ◽  
Modern Information Technology ◽  
Main Factors ◽  
Modern Information ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In recent years, China has stepped up its support to the optimization and development of railways. Meanwhile, the development of modern information technology (IT) has enhanced the economic advantages of railway logistics. To intelligently manage the inventory of railway logistics park (RLP), this paper integrates artificial neural network (ANN) into RLP inventory management. Firstly, the functional demand of RLP inventory management was analyzed comprehensively, and the main factors affecting the inventory demand were divided into different categories. Then, the authors formulated the framework of intelligent inventory management for RLP, and put forward the strategy of continuous periodic inventory monitoring. Finally, a RLP inventory prediction model was constructed based on optimized genetic algorithm (GA) and backpropagation neutral network (BPNN), and proved effective through experiments. The research results provide reference for the application of ANN in inventory management and prediction in other logistics fields.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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