Dam Safety Monitoring Model Based on Neural Network and Time Series

2014 ◽  
Vol 631-632 ◽  
pp. 543-547 ◽  
Author(s):  
Long Long Feng ◽  
Xing Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Deformation Monitoring ◽  
Time Series Model ◽  
Monitoring Data ◽  
Bp Neural Network Model ◽  
Bp Model

The deformation monitoring data of the dam has the typical characters of instability and nonlinearity after being completed and impounding water. To solve the problems, this paper introduces the time series model and BP neural network model to analysis the dam monitoring data. Firstly, time series model was applied to fit and predict and then used the BP neural network model to correct the nonlinear part of residuals. Finally, we can get a series of fitting and predictive value of the monitoring data by combining of above both models. Taking the certain radial displacement value of a measuring point of a certain dam as an example, ARIMA-BP model was established to analyze the data. The result shows: fitting and predictive accuracy of ARIMA-BP model is relatively high and closed to the measured value.

Modified BP Neural Network Model is Used for Odd-Even Discrimination of Integer Number

2013 ◽  
Vol 423-426 ◽  
pp. 2675-2678 ◽  
Author(s):  
Bao Long Hu ◽  
Ji Ren Xu ◽  
Huai Hui Gao ◽  
Ji Hai Liu ◽  
Ke Ren Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Models ◽  
Bp Algorithm ◽  
Integer Number ◽  
Neural Network Models ◽  
Bp Neural Network Model ◽  
Bp Model

This paper introduced the BP neural network model and the BP algorithm in detail, and pointed out the BP neural network existed the defects of local optimal tendency of local optimal, slowed convergence speed etc. Through the modified BP algorithm, we could solve the problems existing in the traditional BP algorithm successfully, simulation results for odd-even discrimination of integer number based on MATLAB BP algorithm show that modified BP model compared with BP model has faster training speed and high study accuracy. Modified BP neural network models is used in practice, as long as it is complementary with effective measures, and we can get satisfactory result completely.

A Neural Network Model of Restrained Recovery for Shape Memory Alloys

2013 ◽  
Vol 650 ◽  
pp. 172-177
Author(s):  
Shuang Wu ◽  
Shou Gen Zhao ◽  
Da Fang Wu ◽  
Xue Mei Yu
Keyword(s):  
Neural Network ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Constitutive Models ◽  
Nonlinear Function ◽  
Bp Neural Network Model ◽  
Bp Model

The methods of constitutive modeling of restrained recovery for Shape memory alloys (SMAs) were described in this paper and experiments were carried out to provide the essential data for the methods. The present mathematical constitutive models are inconvenient for engineering applications. Then a back propagation (BP) neural network model was developed for restrained recovery of SMAs. This BP neural network model can learn the hysteresis of SMAs in the process of heating and cooling based on its properties of nonlinear function mapping and adaptation, and it can predict the complete restrained recovery stress of SMAs with different initial strains. The predicted results obtained from the proposed BP model agree well with the experimental data. Moreover, the proposed BP model is more simple, convenient and low cost compared with the present mathematical constitutive models.

scholarly journals Pricing 50ETF Option Based on Genetic Algorithm BP Model

2018 ◽  
Vol 227 ◽  
pp. 02010
Author(s):  
Yulin Du
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Theory And Practice ◽  
The Neural Network ◽  
Finance Theory ◽  
S Model ◽  
Bp Neural Network Model ◽  
Bp Model

Pricing financial derivatives is focus in finance theory and practice. Comparing to the traditional parameter model pricing method, the neural network method has obvious advantages in solving this problem. In this paper,we will price the option of Shanghai 50ETF based on the improved BP neural network model (GABP). The results show that the effect of neural network is better than that of B-S model, and the accuracy of GABP model is higher than that of BP neural network model and B-S model.

Deformation Forecasting Using a Hybrid Time Series and Neural Network Model

2012 ◽  
Vol 256-259 ◽  
pp. 2343-2346
Author(s):  
Qiang Wang ◽  
Ning Gao ◽  
Wen Zhe Jiao ◽  
Guan Jie Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Autoregressive Model ◽  
Back Propagation ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
High Demand ◽  
Modeling And Forecasting

In order to improve the accuracy and reliability of prediction of deformation monitoring data, a hybrid modeling and forecasting approach based on autoregressive model( AR) and the back-propagation( BP) neural network is proposed to forecast the deformation. The results of experiments show that this method can forecast the deformation precisely, and it is more suitable for those occasions where the deformation monitoring data should meet the high demand.

Model and Analysis of Flexural Properties of Al2O3 Ceramic Prepared by Centrifugal Slip Casting Using BP Neural Network Model

2012 ◽  
Vol 430-432 ◽  
pp. 664-668
Author(s):  
Jing Yuan Yu ◽  
Qiang Li ◽  
Xu Dong Sun
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Process Parameters ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Slip Casting ◽  
Flexural Properties ◽  
Al2o3 Ceramic ◽  
Bp Neural Network Model ◽  
Bp Model

BP neural network model was developed for prediction of the flexural properties of Al2O3 ceramic prepared by centrifugal slip casting. In this paper, the model can well reflect the relationship between the process parameters including solid content of Al2O3 ceramic slurries (w), centrifugal acceleration (v), sintering temperature (T) and flexural properties of sintered products including fracture strength ( ) and fracture toughness (KIC). According to the registered BP model, the effects of w, v and T on and KIC were analyzed. The predicted results agree with the actual data within reasonable experimental error, which shows that the BP model is a practically very useful tool in the flexural properties prediction of the Al2O3 ceramic prepared by centrifugal slip casting. The system reduces the time required for planning and optimizing of centrifugal process parameters.

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.

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