scholarly journals Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Hao Peng ◽  
Han Wu ◽  
Junwu Wang ◽  
Tayfun Dede
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Water Consumption ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Water Demand ◽  
Average Error ◽  
Local Optimum ◽  
Construction Engineering ◽  
Bp Neural Network Model

The scientific and effective prediction of the water consumption of construction engineering is of great significance to the management of construction costs. To address the large water consumption and high uncertainty of water demand in project construction, a prediction model based on the back propagation (BP) neural network improved by particle swarm optimization (PSO) was proposed in the present work. To reduce the complexity of redundant input variables, this model determined the main influencing factors of water demand by grey relational analysis. The BP neural network optimized by PSO was used to obtain the predicted value of the output interval, which effectively solved the shortcomings of the BP neural network model, including its slow convergence speed and easy to fall into local optimum problems. In addition, the water consumption interval data of the Taiyangchen Project located in Xinyang, Henan Province, China, were simulated. According to the results of the case study, there were four main factors that affected the construction water consumption of the Taiyangchen Project, namely, the intraday amount of pouring concrete, the intraday weather, the number of workers, and the intraday amount of wood used. The predicted data were basically consistent with the actual data, the relative error was less than 5%, and the average error was only 2.66%. However, the errors of the BP neural network model, the BP neural network improved by genetic algorithm, and the pluralistic return were larger. Three conventional error analysis tools in machine learning (the coefficient of determination, the root mean squared error, and the mean absolute error) also highlight the feasibility and advancement of the proposed method.

Research on Dynamic Subspace Divided BP Neural Network Identification Method of Color Space Transform Model

2010 ◽  
Vol 174 ◽  
pp. 97-100 ◽  
Author(s):  
Chuan Zhi ◽  
Ling Hua Guo ◽  
Mei Yun Zhang ◽  
Yi Shi
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Color Space ◽  
Local Optimum ◽  
Color Space Conversion ◽  
Network Identification ◽  
Rgb Color Space ◽  
Bp Neural Network Model

In order to improve the precision for BP neural network model color space conversion, this paper takes RGB color space and CIE L*a*b* color space as an example. Based on the input value, the color space is dynamically divided into many subspaces. To adopt the BP neural network in the subspace can effectively avoiding the local optimum of BP neural network in the whole color space and greatly improving the color space conversion precision.

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.

scholarly journals Research on recognition method of cloud precipitation particle shape based on BP neural network

2021 ◽  
Vol 336 ◽  
pp. 06011
Author(s):  
Haonan Dong ◽  
Ruili Jiao ◽  
Minsong Huang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Particle Shape ◽  
Northern China ◽  
Recognition Method ◽  
Cloud Particle ◽  
Particle Images ◽  
Bp Neural Network Model

In order to solve the problem that the shape of cloud particle images measured by airborne cloud imaging probe (CIP) cannot be automatically recognized, this paper proposes an automatic recognition method of cloud and precipitation particle shape based on BP neural network. This method mainly uses a set of geometric parameters which can better describe the shape characteristics of cloud precipitation particles. Based on the cloud precipitation particle images measured by CIP in the precipitation stratiform clouds in northern China, a particle shape data training set and a testing set were constructed to train and verify the effect of the selected BP neural network model. The selected BP neural network model can classify the cloud particle image into tiny, column, needle, dendrite, aggregate, graupel, sphere, hexagonal and irregular. Utilizing the field campaign data measured by CIP, the habit identified results by the improved Holroyd method and by the selected BP neural network model were compared, which shows that the accuracy of BP neural network method is better than that of improved Holroyd method.

scholarly journals Analysis and prediction on the cutting process of constrained damping boring bars based on PSO-BP neural network model

2017 ◽  
Vol 19 (2) ◽  
pp. 878-893 ◽  
Author(s):  
Xianming Chen ◽  
Tieliu Wang ◽  
Mingming Ding ◽  
Jing Wang ◽  
Jianqing Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Cutting Process ◽  
Constrained Damping ◽  
Bp Neural Network Model
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