Application of BP Neural Network to Tendency Prediction of State Monitoring Data

2014 ◽  
Vol 556-562 ◽  
pp. 2744-2747 ◽  
Author(s):  
Xu De Cheng ◽  
Hong Li Wang ◽  
Bing Xu ◽  
Wei Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Network Theory ◽  
Monitoring Data ◽  
Data Sets ◽  
State Monitoring ◽  
Multiple Data ◽  
Multiple Data Sets ◽  
Neural Network Theory ◽  
Bp Neural Network Model

BP neural network model for state monitoring data tendency prediction is constructed based upon neural network theory, and simulation programming is achieved with MATLAB. In the experiment, multiple data sets are selected for training and testing of the network to prove the validity of algorithm and model.

BP Neural Network Model on Choice of Project Manager for Highway Slope Treatment

2014 ◽  
Vol 505-506 ◽  
pp. 274-277
Author(s):  
Bin Wang ◽  
Yong Tao Gao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Theory ◽  
Influence Factors ◽  
Project Manager ◽  
Slope Treatment ◽  
Neural Network Theory ◽  
Bp Neural Network Model

To get the quantified indexes of comprehensive capacity about project manager, based on the modal on artificial neural network theory, different influence factors about choice of project manager for highway slope treatment were analyzed , identified, quantified and evaluated , then comprehensive capacity of the manager were analyzed. Such procedure provided a new method for choice of project manager for highway slope treatment.

scholarly journals Transient Power Quality Recognition Based on BP Neural Network Theory

2012 ◽  
Vol 16 ◽  
pp. 1386-1392 ◽  
Author(s):  
Xu Tongyu ◽  
Zheng wei ◽  
Sun Peng ◽  
Zhang Qin
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Bp Neural Network ◽  
Network Theory ◽  
Neural Network Theory ◽  
Transient Power Quality

Creep Behavior Prediction Research of T92 Steel Based on BP Neural Network

2013 ◽  
Vol 467 ◽  
pp. 203-207
Author(s):  
Jian Liu
Keyword(s):  
Neural Network ◽  
Creep Behavior ◽  
Bp Neural Network ◽  
Network Theory ◽  
Experimental Results ◽  
Behavior Prediction ◽  
Forecast Precision ◽  
Network Method ◽  
Simulation Results ◽  
Neural Network Theory

Based on the BP neural network theory, the creep rate prediction model of T92 steel was established under multiple stress levels. Obtained the experimental results and using the model, the experimental results were trained. The results show that the simulation results match the measured results well with a high forecast precision. The BP neural network method can serve as research on T92 steel creep behavior.

Harmonic Monitoring Based on BP Neural Network

2012 ◽  
Vol 532-533 ◽  
pp. 1354-1358
Author(s):  
Bai Fen Liu ◽  
Yun Chen ◽  
Xian Wu Fang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Bp Neural Network ◽  
Network Theory ◽  
Harmonic Suppression ◽  
Matlab Simulation ◽  
Time Data ◽  
Monitoring Model ◽  
Training Capacity ◽  
Neural Network Theory

This article applies the BP neural network theory into the measuring of harmonics, through the model automatically learn training capacity to create real-time monitoring model of harmonic. And that I am using matlab simulation, to obtained harmonic simulation data. Through this simulation technology to monitor the harmonic and obtain real-time data, the data can provide reference for harmonic suppression.

Predicition of GRT Fiber-Rubberized Haydite Concrete Compressive Strength Based on Multiple Regreeion Analysis and BP Neural Network

2012 ◽  
Vol 472-475 ◽  
pp. 60-65
Author(s):  
Bing Hua Xia ◽  
Yuan Cai Liu ◽  
De Bin Zhu
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Multiple Regression ◽  
Test Data ◽  
Bp Neural Network ◽  
Intensity Level ◽  
Regression Equations ◽  
Strength Based ◽  
Neural Network Theory ◽  
Bp Neural Network Model

Experiment with intensity level for the LC30 ceramsite concrete as the research object, changing the content of cement, GRT fiber, rubber powder by the orthogonal test to configure GRT fiber—rubberized haydite concrete samples, maintenance samples 7d and 28d in standard conditions and respectively testing their standard compressive strength. Through the analysis of the test data, using multiple regression analysis established the GRT fiber—rubberized haydite concrete 7d and 28d standard compressive strength regression formulas.By means of BP neural network theory combine MATLAB programme established GRT fiber—rubberized haydite concrete 7d and 28d standard compressive strength neural network model.Finally using 3 groups new test data to compare the value of multiple regression equations and BP neural network’s predicted value.The results indicate that the multiple regression equations and BP neural network model are availabled.

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.

The University Students Entrepreneurship Evaluation System Based on BP Neural Network

2014 ◽  
Vol 556-562 ◽  
pp. 6742-6745 ◽  
Author(s):  
Kai Zhou ◽  
Meng Ting Ji
Keyword(s):  
Neural Network ◽  
University Students ◽  
Knowledge Economy ◽  
Bp Neural Network ◽  
Network Theory ◽  
Evaluation System ◽  
Entrepreneurship Education ◽  
The Creation ◽  
Neural Network Theory ◽  
The University

In the knowledge economy period, nowadays the entrepreneurship of university students more and more attracts attention of society and universities. However, the entrepreneurship education of university students in China currently remains at the exploration stage and the entrepreneurship evaluation system of university students is not ideal. The absence of the entrepreneurship evaluation system is the important factors restricting the development of employment of university students’ entrepreneurship education, and the creation of university students’ entrepreneurship evaluation system is the center of the evaluation system as a whole and key. According to the characteristics of BP neural network, the paper presents an entrepreneurship evaluation system of university students by applying the BP neural network theory. It draws some constructive conclusions and suggestions as results.

The Establishment of Land Appraisal Model in Xiamen Based on BP Neural Network

2012 ◽  
Vol 518-523 ◽  
pp. 6084-6087
Author(s):  
Qing Ye ◽  
Ya Yi Su ◽  
Fei Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Network Theory ◽  
Evaluation Model ◽  
Land Evaluation ◽  
Bp Network ◽  
The Neural Network ◽  
Hidden Layer ◽  
Neural Network Theory ◽  
Error Index

Establish the land evaluation model of Xiamen by means of BP neural network theory, taking 2007-2009 land evaluation cases of Xiamen as examples. Through statistical analysis, we find that the neural network which has 9 net work hidden layer nodes and 19% of maximal error index is more suitable for Xiamen land price assessment than others. Empirical analysis shows that the model has a good generalization ability, which can be used for land evaluation practices. The results indicates that the properties of autonomous learning of BP network can reduce the subjective factors of appraiser in land evaluation , also, the network has the advantage of simple and quick calculation.

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