Applied Research Of BP Neural Network In Earthquake Prediction

2012 ◽  
Vol 204-208 ◽  
pp. 2449-2454 ◽  
Author(s):  
Wu Sheng Hu ◽  
Hong Lin Nie ◽  
Hao Wang
Keyword(s):  
Neural Network ◽  
Earthquake Prediction ◽  
Bp Neural Network ◽  
Integrated Model ◽  
Earthquake Magnitude ◽  
Linear Regression Method ◽  
Network Technology ◽  
Short Term ◽  
Non Linear ◽  
Neural Network Technology

Nowadays, earthquake prediction is still a worldwide scientific problem, especially the prediction for short-term and imminent earthquake has no substantial breakthroughs. BP neural network technology has a strong non-linear mapping function which could better reflect the strong non-linear relationship between earthquake precursors and the time and the magnitude of a potential earthquake. In this paper, we selected the region of Beijing as the research area and 3 months as the prediction period. Based on BP neural network and integrated with the conventional linear regression method, a regional short-term integrated model was established, which gives the quantitative prediction for the earthquake magnitude. The results show that the earthquake magnitude prediction RMSE (root mean square error) of the integrated model reaches ± 0.28 Ms. Compared with conventional methods, the integrated model improves significantly. The new model has a good prospect to use BP neural network technology for earthquake prediction.

Prediction of EVA Plastic's Expansion Ratio Based on BP Neural Network

2013 ◽  
Vol 364 ◽  
pp. 529-533
Author(s):  
Lai Fa Zhu ◽  
Bin Liu ◽  
Jian Wen Xu
Keyword(s):  
Neural Network ◽  
Experimental Design ◽  
Bp Neural Network ◽  
Thin Sheet ◽  
Injection Pressure ◽  
Expansion Ratio ◽  
Network Technology ◽  
Sample Data ◽  
Different Diameters ◽  
Neural Network Technology

Combined with Taguchi method of experimental design and BP neural network technology, select process parameters including molding temperature, molding time and injection pressure, Taguchi experiments are respectively arranged for different diameters of sphere and cylinder, different side lengths of tri-prism and quadrangular prism, different thicknesses of thin sheet without hole and with hole etc. molds, then these experiment results are used as neural network sample data, and expansion ratio of EVA plastic can be predicted more accurately after neural network is trained.

scholarly journals Short-Term Wind Power Prediction Using GA-BP Neural Network Based on DBSCAN Algorithm Outlier Identification

Processes ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 157 ◽  
Author(s):  
Pei Zhang ◽  
Yanling Wang ◽  
Likai Liang ◽  
Xing Li ◽  
Qingtian Duan
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Bp Neural Network ◽  
Linear Regression Method ◽  
Power Prediction ◽  
Short Term ◽  
Outlier Identification ◽  
Dbscan Algorithm ◽  
Wind Power Prediction

Accurately predicting wind power plays a vital part in site selection, large-scale grid connection, and the safe and efficient operation of wind power generation equipment. In the stage of data pre-processing, density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify the outliers in the wind power data and the collected wind speed data of a wind power plant in Shandong Province, and the linear regression method is used to correct the outliers to improve the prediction accuracy. Considering the important impact of wind speed on power, the average value, the maximum difference and the average change rate of daily wind speed of each historical day are used as the selection criteria to select similar days by using DBSCAN algorithm and Euclidean distance. The short-term wind power prediction is carried out by using the similar day data pre-processed and unprocessed, respectively, as the input of back propagation neural network optimized by genetic algorithm (GA-BP neural network). Analysis of the results proves the practicability and efficiency of the prediction model and the important role of outlier identification and correction in improving the accuracy of wind power prediction.

Research on the Prediction Method for Library Reader Flow Based on Evolutionary Neural Network

2014 ◽  
Vol 543-547 ◽  
pp. 2128-2132
Author(s):  
Ping Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Network Technology ◽  
Important Work ◽  
Evolutionary Neural Network ◽  
Flow Prediction ◽  
Risk Period ◽  
Neural Network Technology

It is an important work for modern libraries to predict reader flow. With the help of reader flow, library staff can grasp the change regulation of readers, allocate tasks rationally and take steps ahead of time in high-risk period. Because of reader flows typical non-linear characteristics, evolutionary neural network technology is introduced in this research so as to improve the accuracy of reader flow prediction. A prediction method for library reader flow based on evolutionary neural network is proposed. Genetic algorithm is used to optimize and design BP neural network firstly, then evolutionary neural network is used to predict reader flow. The experimental results show that evolutionary neural network is an effective tool for us to predict library reader flow. We can realize an accurate prediction for library reader flow by this method.

Lightning Trip Warning Based on GA-BP Neural Network Technology

2020 ◽  
Author(s):  
Gu Su ◽  
Tang Chongwang ◽  
Deng Zhiyong ◽  
Wang Zhenyu ◽  
Wang Jia ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Network Technology ◽  
Neural Network Technology

Synthesis Error Analysis of NC Manufacturing and Error Prediction Modelling Based on BP Neural Network Technology

2011 ◽  
Vol 314-316 ◽  
pp. 1254-1257
Author(s):  
Hao Fan ◽  
Hang Li ◽  
Dong Hong Si ◽  
Yu Jun Xue ◽  
Guo Feng Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Prediction Model ◽  
Cutting Force ◽  
Bp Neural Network ◽  
Error Prediction ◽  
Network Technology ◽  
Element Analysis ◽  
Machining Center ◽  
Neural Network Technology

The method was proposed by use of the finite element analysis software ABAQUS and the BP neural network technology to build a synthesis error prediction model of a machining-center. Firstly the finite element model of a vertical machining center CINCINNATI ARROW750 was created by use of ABAQUS ,and the cutting force induced error was analyzed which resulted from the deformation of the machining-center’s components that was caused by the cutting force ;Secondly the geometric error of the machining-center was measured by use of the laser interometer,and the sample of synthesis error was obtained. Finally the synthesis error prediction model was obtained by use BP neural network,and through the comparison of predicted value and actual value of 25 groups of samples, the feasibility of error prediction model was verified.

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