scholarly journals Research on Geological Disaster Forecast Method Based on PCA Structure BP Model

2021 ◽  
Vol 2083 (4) ◽  
pp. 042004
Author(s):  
Zhangbao Luan
Keyword(s):  
Prediction Method ◽  
Geological Hazard ◽  
Short Term ◽  
Calculation Results ◽  
Neural Network Prediction ◽  
Forecast Models ◽  
Network Prediction ◽  
Bp Model ◽  
Term Objective ◽  
Historical Samples

Abstract The BP neural network prediction method constructed by PCA and the geological hazard prediction method based on the MM5 numerical model were used to establish geological hazard classification short-term objective forecast models. The calculation results show that these two objective forecast methods have a good fitting effect on historical samples. The independent sample’s trial report effect is also good; based on the above two objective forecasting methods, through correction, the comprehensive forecast product is finally obtained.

scholarly journals Wave Overtopping Database As the Starting Point for a Neural Network Prediction Method

2004 ◽  
Author(s):  
H. Verhaeghe ◽  
J. W. van der Meer ◽  
G.-J. Steendam ◽  
P. Besley ◽  
L. Franco ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Wave Overtopping ◽  
Starting Point ◽  
Neural Network Prediction ◽  
Network Prediction

scholarly journals Research on bus elastic departure interval based on Wavelet Neural Network

2021 ◽  
Vol 261 ◽  
pp. 03052
Author(s):  
Zhe Lv ◽  
Jiayu Zou ◽  
Zhongyu Zhao
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Wavelet Neural Network ◽  
Economic Costs ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Intelligent Optimization Algorithm ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Important Basis

In recent years, more and more people choose to travel by bus to save time and economic costs, but the problem of inaccurate bus arrival has become increasingly prominent. The reason is the lack of scientific planning of departure time. This paper takes the passenger flow as an important basis for departure interval, proposes a passenger flow prediction method based on wavelet neural network, and uses intelligent optimization algorithm to study the bus elastic departure interval. In this paper, the wavelet neural network prediction model and the elastic departure interval optimization model are established, and then the model is solved by substituting the data, and finally the theoretical optimal departure interval is obtained.

scholarly journals Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1662
Author(s):  
Wei Hao ◽  
Feng Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
High Speed ◽  
Prediction Method ◽  
High Speed Train ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Trained Neural Network

Predicting the axle temperature states of the high-speed train under operation in advance and evaluating working states of axle bearings is important for improving the safety of train operation and reducing accident risks. The method of monitoring the axle temperature of a train under operation, combined with the neural network prediction method, was applied. A total of 36 sensors were arranged at key positions such as the axle bearings of the train gearbox and the driving end of the traction motor. The positions of the sensors were symmetrical. Axle temperature measurements over 11 days with more than 38,000 km were obtained. The law of the change of the axle temperature in each section was obtained in different environments. The resultant data from the previous 10 days were used to train the neural network model, and a total of 800 samples were randomly selected from eight typical locations for the prediction of axle temperature over the following 3 min. In addition, the results predicted by the neural network method and the GM (1,1) method were compared. The results show that the predicted temperature of the trained neural network model is in good agreement with the experimental temperature, with higher precision than that of the GM (1,1) method, indicating that the proposed method is sufficiently accurate and can be a reliable tool for predicting axle temperature.

scholarly journals Deep Learning Neural Network Prediction Method Improves Proteome Profiling of Vascular Sap of Grapevines during Pierce’s Disease Development

Biology ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 261
Author(s):  
Cíntia Helena Duarte Sagawa ◽  
Paulo A. Zaini ◽  
Renata de A. B. Assis ◽  
Houston Saxe ◽  
Michelle Salemi ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Prediction Method ◽  
Pierce's Disease ◽  
Defense Proteins ◽  
Proteome Profiling ◽  
Pierce’S Disease ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Deep Learning Neural Network

Plant secretome studies highlight the importance of vascular plant defense proteins against pathogens. Studies on Pierce’s disease of grapevines caused by the xylem-limited bacterium Xylella fastidiosa (Xf) have detected proteins and pathways associated with its pathobiology. Despite the biological importance of the secreted proteins in the extracellular space to plant survival and development, proteome studies are scarce due to methodological challenges. Prosit, a deep learning neural network prediction method is a powerful tool for improving proteome profiling by data-independent acquisition (DIA). We explored the potential of Prosit’s in silico spectral library predictions to improve DIA proteomic analysis of vascular leaf sap from grapevines with Pierce’s disease. The combination of DIA and Prosit-predicted libraries increased the total number of identified grapevine proteins from 145 to 360 and Xf proteins from 18 to 90 compared to gas-phase fractionation (GPF) libraries. The new proteins increased the range of molecular weights, assisted in the identification of more exclusive peptides per protein, and increased identification of low-abundance proteins. These improvements allowed identification of new functional pathways associated with cellular responses to oxidative stress, to be investigated further.

Prediction and Analysis of Strawberry Moisture Content based on BP Neural Network Model

2020 ◽  
Vol 13 (4) ◽  
pp. 657-671
Author(s):  
Wei Jiang ◽  
Hongmei Xu ◽  
Elnaz Akbari ◽  
Jiang Wen ◽  
Shuang Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Moisture Content ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Principal Component ◽  
Spectral Difference ◽  
Model Based ◽  
Neural Network Prediction ◽  
Network Prediction

Background: Moisture content is one of the most important indicators for the quality of fresh strawberries. Currently, several methods are usually employed to detect the moisture content in strawberry. However, these methods are relatively simple and can only be used to detect the moisture content of single samples but not batches of samples. Besides, the integrity of the samples may be destroyed. Therefore, it is important to develop a simple and efficient prediction method for strawberry moisture to facilitate the market circulation of strawberry. Objective: This study aims to establish a novel BP neural network prediction model to predict and analyze strawberry moisture. Methods: Toyonoka and Jingyao strawberries were taken as the research objects. The hyperspectral technology, spectral difference analysis, correlation coefficient method, principal component analysis and artificial neural network technology were combined to predict the moisture content of strawberry. Results: The characteristic wavelengths were highly correlated with the strawberry moisture content. The stability and prediction effect of the BP neural network prediction model based on characteristic wavelengths are superior to those of the prediction model based on principal components, and the correlation coefficients of the calibration set for Toyonaka and Jingyao respectively reached up to 0.9532 and 0.9846 with low levels of standard deviations (0.3204 and 0.3010, respectively). Conclusion: The BP neural network prediction model of strawberry moisture has certain practicability and can provide some reference for the on-line and non-destructive detection of fruits and vegetables.

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