scholarly journals Prediction of Seepage Pressure Based on Memory Cells and Significance Analysis of Influencing Factors

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhao Mengdie ◽  
Haifeng Jiang ◽  
Mengdie Zhao ◽  
Yajing Bie
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Rough Set Theory ◽  
Short Term Memory ◽  
Seepage Analysis ◽  
Training Time ◽  
Seepage Pressure ◽  
Memory Network ◽  
Bp Neural Network Model ◽  
Safety And Stability

Seepage analysis is always a concern in dam safety and stability research. The prediction and analysis of seepage pressure monitoring data is an effective way to ensure the safety and stability of dam seepage. With the timeliness of a change in a monitoring value and lag due to external influences, a RS-LSTM model written in Python is developed in this paper which combines rough set theory (RS) and the long- and short-term memory network model (LSTM). The model proposed calculates the prediction score of the seepage pressure of a dam experiencing multiple effects by preordering factor importance values to eliminate the interference of redundant factors. A case study shows that the water level, rainfall, temperature, and duration are all factors that affect the seepage pressure, and their importance values decrease successively. Thus, the seepage pressure of a dam can be predicted with a determination coefficient R2 of 0.96. Compared with the recurrent neural network (RNN) model and BP neural network model, the training time of the RS-LSTM model proposed is 6.37 s, and the operation efficiency is 41% and 59% higher than that of the RNN and BP models, respectively. The mean relative error is also 3.00%, which is 50% lower than that of the RNN model and 31% lower than that of the BP model. Based on these results, this model has the advantages of fast computation speed and high accuracy in prediction.

Application of Elm Neural Network in Dam Displacement Early Warning Model

2013 ◽  
Vol 864-867 ◽  
pp. 2363-2366
Author(s):  
Tu Tu ◽  
Feng Zhu ◽  
Ai Wu Cao ◽  
Lu He ◽  
Geng Ying
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Early Warning ◽  
Bp Neural Network ◽  
Training Time ◽  
Multiple Factors ◽  
Bp Neural Network Model ◽  
Warning Model ◽  
Temperature Water

The dam displacement is related to multiple factors such as time, temperature, water level and etc. And it presents a strong nonlinear and certain randomness.Neural network model because of its inherent characteristics can better simulate the dam displacement.Nowadays,It has methods to estimate the displacement of the dam by constructing physical model and BP neural network model.But BP neural network's training time is too long and the forecast effect is not very good.So this paper introduces Elm neural network model,establishs Elm neural network model of dam displacement early warning considering multiple factors to estimate the displacement.By a simple example and compared with BP neural network model to reflect the rationality and scientificity of this method.

A Fault Diagnosis Intelligent Algorithm Based on Improved BP Neural Network

2019 ◽  
Vol 33 (09) ◽  
pp. 1959028 ◽  
Author(s):  
Pingfeng Liu ◽  
Wang Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Reference Value ◽  
Intelligent Algorithm ◽  
Neural Network Models ◽  
Training Time ◽  
Bp Neural Network Model

The fault diagnosis intelligent algorithm makes full use of the associative memory and pattern recognition function of the neural network to compare the abnormal value of various parameters of the engine fault with the reference value of the known fault mode, which can shorten the fault diagnosis time and improve the diagnosis efficiency. BP neural network model as one of the most widely used neural network models in the world is of significance to solve nonlinear complex problems. Of course, there are also some deficiencies in it, such as long training time and ease to trap into local minimum. This paper utilized the global search advantage of genetic algorithm to optimize the optimal weight and threshold value of BP neural network. Furthermore, an improved BP neural network was put forward, which is greatly improved in stability, generalization and convergence rate. Taking fault diagnosis of automobile engine as an example, a simulation experiment was carried out on the established model. The research results indicate that improved neural network model owns a higher accuracy than pure GA model or BP neural network model (with an average accuracy improved by 19.04% than traditional model), and its effect is satisfactory.

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.

scholarly journals Influence of hinge length and distribution number on the camber and cornering properties of a non-pneumatic tire

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098468
Author(s):  
Xianbin Du ◽  
Youqun Zhao ◽  
Yijiang Ma ◽  
Hongxun Fu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Adaptive Learning ◽  
High Speed ◽  
Back Propagation ◽  
Lateral Force ◽  
Learning Ability ◽  
Vehicle Performance ◽  
Neural Network Theory ◽  
Bp Neural Network Model

The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

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