scholarly journals Simulation of Crop Evaportranspiration Based on BP Neural Network Model and Grey Relational Analysis

2012 ◽  
Vol 2 (1) ◽  
pp. 15-21
Author(s):  
Liang Ma ◽  
Feng Liu ◽  
Liangliang Chen ◽  
Ming Hong
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Grey Relational Analysis ◽  
Relational Analysis ◽  
Bp Neural Network Model ◽  
Grey Relational

Estimating nitrogen contents of apple leaves based on hyperspectral parameter models at different phenophases

2016 ◽  
Vol 6 (2) ◽  
pp. 942-952
Author(s):  
Xicun ZHU ◽  
Zhuoyuan WANG ◽  
Lulu GAO ◽  
Gengxing ZHAO ◽  
Ling WANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Shoot Growth ◽  
Hyperspectral Data ◽  
Estimation Model ◽  
Apple Leaves ◽  
Bp Neural Network Model ◽  
Nitrogen Contents

The objective of the paper is to explore the best phenophase for estimating the nitrogen contents of apple leaves, to establish the best estimation model of the hyperspectral data at different phenophases. It is to improve the apple trees precise fertilization and production management. The experiments were done in 20 orchards in the field, measured hyperspectral data and nitrogen contents of apple leaves at three phenophases in two years, which were shoot growth phenophase, spring shoots pause growth phenophase, autumn shoots pause growth phenophase. The study analyzed the nitrogen contents of apple leaves with its original spectral and first derivative, screened sensitive wavelengths of each phenophase. The hyperspectral parameters were built with the sensitive wavelengths. Multiple stepwise regressions, partial least squares and BP neural network model were adopted in the study. The results showed that 551 nm, 716 nm, 530 nm, 703 nm; 543 nm, 705 nm, 699 nm, 756 nm and 545 nm, 702 nm, 695 nm, 746 nm were sensitive wavelengths of three phenophases. R551+R716, R551*R716, FDR530+FDR703, FDR530*FDR703; R543+R705, R543*R705, FDR699+FDR756, FDR699*FDR756and R545+R702, R545*R702, FDR695+FDR746, FDR695*FDR746 were the best hyperspectral parameters of each phenophase. Of all the estimation models, the estimated effect of shoot growth phenophase was better than other two phenophases, so shoot growth phenophase was the best phenophase to estimate the nitrogen contents of apple leaves based on hyperspectral models. In the three models, the 4-3-1 BP neural network model of shoot growth phenophase was the best estimation model. The R2 of estimated value and measured value was 0.6307, RE% was 23.37, RMSE was 0.6274.

scholarly journals Regional logistics demand forecasting: a BP neural network approach

2021 ◽  
Author(s):  
Lijuan Huang ◽  
Guojie Xie ◽  
Wende Zhao ◽  
Yan Gu ◽  
Yi Huang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Demand Forecasting ◽  
Indicator System ◽  
Regional Logistics ◽  
Logistics Enterprises ◽  
Logistics Demand ◽  
Bp Neural Network Model

AbstractWith the rapid development of e-commerce, the backlog of distribution orders, insufficient logistics capacity and other issues are becoming more and more serious. It is very significant for e-commerce platforms and logistics enterprises to clarify the demand of logistics. To meet this need, a forecasting indicator system of Guangdong logistics demand was constructed from the perspective of e-commerce. The GM (1, 1) model and Back Propagation (BP) neural network model were used to simulate and forecast the logistics demand of Guangdong province from 2000 to 2019. The results show that the Guangdong logistics demand forecasting indicator system has good applicability. Compared with the GM (1, 1) model, the BP neural network model has smaller prediction error and more stable prediction results. Based on the results of the study, it is the recommendation of the authors that e-commerce platforms and logistics enterprises should pay attention to the prediction of regional logistics demand, choose scientific forecasting methods, and encourage the implementation of new distribution modes.

Misfire Diagnosis of Diesel Engine Based on Short-Time Vibration Characters

2010 ◽  
Vol 34-35 ◽  
pp. 301-305
Author(s):  
Zhao Qian Zhu ◽  
Jue Yang ◽  
Xiao Ming Zhang ◽  
Xiao Lei Li
Keyword(s):  
Neural Network ◽  
Diesel Engine ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Monitoring Test ◽  
Bp Neural Network Model ◽  
Short Time

This paper studied misfire diagnosis of diesel engine based on short-time vibration characters. Misfire of diesel engine was simulated by the vibration monitoring test. Cylinder vibration signal and top center signal were collected under different states. The short-time vibration signal of each cylinder was intercepted according to the diesel combustion sequence, effective value was calculated, and BP Neural Network model built with this character was used to diagnose diesel misfire. The result shows that this method can locate the misfire cylinder effectively, and it is meaningful for guiding the detection and repair of vehicles.

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