Application of Artificial Neural Network in GPS Height Transformation

2014 ◽  
Vol 501-504 ◽  
pp. 2162-2165 ◽  
Author(s):  
Bo Fu ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Nonlinear Coefficient ◽  
Height Anomaly ◽  
Engineering Practice ◽  
Model Structure ◽  
Normal Height ◽  
Sample Data ◽  
Geodetic Height ◽  
Residual Errors

GPS technology has been widely used since it was put into use. At present, the plane locational accuracy of GPS can already achieve millimeter level. But in terms of height, in order to apply the GPS ellipsoidal height in engineering practice, the geoid seperation or height anomaly of the corresponding point must be achieved to transform GPS geodetic height to normal height. In this paper, by taking 12 points from the national GPS control network of Xuxiang Village of Haining City as sample data, a BP neural network using 3-4-2-1 model structure is adopted and a nonlinear coefficient 1.1 is added in the response function. The height anomalies of the 5 points of the test set are calculated and the residual errors are achieved by comparing with the measured values. The internal and external coincidence accuracies of the model are 0.824cm and 0.922cm separately. The result shows that the model can completely meet the precision requirement of the fourth-grade leveling survey and can be used to transform the heights of the study area.

Prediction Model of Flow-Induced Noise in Large-Scale Centrifugal Pumps Based on BP Neural Network

2017 ◽  
Author(s):  
Chang Guo ◽  
Ming Gao ◽  
Peixin Dong ◽  
Yuetao Shi ◽  
Fengzhong Sun
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Centrifugal Pumps ◽  
Output Layer ◽  
Sample Data ◽  
Input Layer ◽  
Flow Induced Noise ◽  
Hidden Layer

As one kind of serious environmental problems, flow-induced noise in centrifugal pumps pollutes the working circumstance and deteriorates the performance of pumps, meanwhile, it always changes drastically under various working conditions. Consequently, it is extremely significant to predict flow-induced noise of centrifugal pumps under various working conditions with a practical mathematical model. In this paper, a three-layer back propagation (BP) neural network model is established and the number of input, hidden and output layer node is set as 3, 6 and 1, respectively. To be specific, the flow rate, rotational speed and medium temperature are chosen as input layer, and the corresponding flow-induced noise evaluated by average of total sound pressure level (A_TSPL) as output layer. Furthermore, the tansig function is used to act as transfer function between the input layer and hidden layer, and the purelin function is used between hidden layer and output layer. The trainlm function based on Levenberg-Marquardt algorithm is selected as the training function. By using a large number of sample data, the training of the network model and prediction research are accomplished. The results indicate that good correlation is established among the sample data, and the predictive values show great consistence with simulation ones, of which the average relative error of A_TSPL in process of verification is 0.52%. The precision of the model can satisfy the requirement of relevant research and engineering application.

Establishment and Application of Risk Forecast Model of Construction Project Based upon BP Neural Network

2014 ◽  
Vol 945-949 ◽  
pp. 3056-3059 ◽  
Author(s):  
Xin Xin Li
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Minimal Cost ◽  
Input And Output ◽  
Sample Data ◽  
Risk Forecast ◽  
The Relationship ◽  
And Training

Risk management is a kind of activity by economic unit to obtain the maximal safety guarantee at the minimal cost through the identification and measuring of risk, in which reasonable economic and technical means are defined to cope with the risk, and it is also a process of estimating, evaluating and preventing the risk. Based upon the collection and normalization of sample data, determination and training of network structure, by identifying the relationship between input and output, BP neural network establishes risk forecast model of project, then the sample is tested and risk forecast model is validated.

Emulation and Prediction of the Cold Roll Forming Force

2012 ◽  
Vol 472-475 ◽  
pp. 206-213 ◽  
Author(s):  
Jin Gang Wu ◽  
Qiang Li ◽  
Yu Yan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Roll Forming ◽  
Cold Roll Forming ◽  
Forming Force ◽  
Cold Roll ◽  
Sample Data ◽  
Experiment Method ◽  
Main Factors ◽  
Single Factor Experiment

In order to optimize the craft parameter of cold roll forming, this paper puts forward a new method reflect the law between cold roll forming force and craft parameter. The method combines artificial neural network and finite element emulation for cold roll forming, and begins with analyzing the cold roll forming to decide the main factors that have an impact on roll forming force. According to the single factor experiment method, the paper establishes the roll forming emulation model to simulate, and a BP neural network is constructed with the sample data from the simulation.

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.

Predictive analysis of user behavior of E-commerce platform based on machine learning image algorithm in internet of things environment

2021 ◽  
pp. 1-8
Author(s):  
Jiacheng Ni
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Bp Neural Network ◽  
Theoretical Foundation ◽  
User Behavior ◽  
Rbf Neural Network ◽  
Model Structure ◽  
Internet Of Things Environment ◽  
Detailed Method ◽  
Overall Performance

With the development of the Internet, the rise of e-commerce has changed the shopping habits of most people. The research of this article is mainly divided into three parts. The first part is the theoretical foundation and core concept research. The second part of this article is a detailed method of establishing a predictive model based on machine learning image algorithms. In addition to reclassifying features, image algorithms are also used to optimize the model structure. The third part is the experimental results and analysis. After comparing with BP neural network and RBF neural network, through data analysis, the prediction model in this paper greatly improves the prediction accuracy and time, and the overall performance has a breakthrough.

scholarly journals Forecast of Football League Sales Effect Based on BP Neural Network

2020 ◽  
Author(s):  
Zhubo Xu ◽  
Weifeng Qin
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Percentage Difference ◽  
The Neural Network ◽  
Sample Data ◽  
Real Value ◽  
Ancient Times ◽  
The Difference

Abstract Football is one of the sports that is loved by people all over the world. Its sales ability in the league should not be underestimated. Moreover, football has been developed in our country since ancient times and has a huge fan base, and fans are the main target of football league sales. Predicting the sales effect of the football league is helpful for the seller to formulate a suitable sales strategy and avoid the problem of product surplus. This article mainly introduces the prediction research of football league sales effect based on BP neural network, and intends to provide ideas and methods for predicting the sales effect of football league. This paper puts forward the basic method of the sales effect prediction of the football league and the BP neural network football league sales effect prediction method to analyze and predict the sales effect of the football league. In addition, the steps of establishing BP neural network design, building BP neural network football league sales effect prediction model and applying BP neural network football league sales effect prediction model are also proposed. The experimental results of this article show that the difference between the fitting part of the neural network model and the real value of the football league sales effect sample data is in the range of , the error percentage difference is small, and the prediction results are valid。

Based on Genetic Algorithms and Artificial Neural Network Algorithms to Optimize the Structure Design and Implementation of Crane

2012 ◽  
Vol 490-495 ◽  
pp. 2120-2124
Author(s):  
Xiao Mo Yu ◽  
Xiao Ping Liao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Dynamic Behavior ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Structure System ◽  
Sample Data

This paper by using the finite element method, orthogonal test method, BP neural network and genetic algorithm to optimization of crane structure system. A dynamic optimal computational model for the complex structure system with genetic algorithm (GA)and BP neural network(NN)was presented．Instead of the traditional finite element model，this model can be used for the fast re-analysis for the vibration system．Firstly，the harmonic response kinetics analysis can be processed on a crane structure system and can find out them ode frequency which has the strongest effect on the system dynamic behavior．Secondly，from the sensitivity analysis，the design variables which are more sensitive to the system dynamic behavior can be confirmed as the input variables． Then an orthogonal experimentation was used in choosing the training sample data and the sample data was calculated through the finite element model．The artificial neural network model which presented the dynamic behavior of the structure vibration was established．At last，the neural network model will be optimized through the generic algorithm and the optimal parameters of the structure dynamic behavior will be obtained．

Grounding Grids Fault Diagnosis Based on PCA-BP Neural Network

2012 ◽  
Vol 516-517 ◽  
pp. 1774-1778 ◽  
Author(s):  
Xian Feng Liu ◽  
Min Fang Peng
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Principal Component ◽  
Neural Net ◽  
Analysis Method ◽  
Sample Data ◽  
Grounding Grids ◽  
Diagnosis Accuracy

In order to solve the problem of needing to build accurate mathematical model in grounding grids fault diagnosis ,a new method for applying to the study of grounding grids fault diagnosis,which was based on BP neural network and improved by PCA theory，was introduced.The PCA(Principal component analysis)method was incorporated into the network,which not only solved the linear correlation of the input, but also simplified the network structure and reduced the parameters of neural net input. It realized optimum compression of fault sample data and enhanced classification speed and precision. The experimental results demonstrated that the method could decrease the number of the network input nerve cells effectively, and enhanced study efficiency and diagnosis accuracy. The way had very good fault distinguishing ability and vast prospect.

Local Quasi-Geoid Refinement Based on Spherical Cap Harmonic Model

2012 ◽  
Vol 226-228 ◽  
pp. 1947-1950 ◽  
Author(s):  
Jin Yun Guo ◽  
Shu Yang Wang ◽  
Guo Wei Li ◽  
Wei Hua Mao ◽  
Yuan Ming Ji
Keyword(s):  
Height Anomaly ◽  
Practical Case ◽  
Harmonic Model ◽  
Geoid Model ◽  
Normal Height ◽  
Spherical Cap ◽  
Geodetic Height ◽  
Gps Leveling ◽  
Spherical Cap Harmonic Model ◽  
Selection Of

The local quasi-geoid model up to centimeter precision has became the basic requirement for the development of modern surveying and mapping science. There are a variety of models can be used for the quasi-geoid refinement, including the spherical cap harmonic model (SCH). This paper studies the theory of SCH to get the spherical cap harmonic expression to fit the height anomaly in the least squares sense, which is to achieve the transformation between the geodetic height and the normal height. We also discuss the selection of the maximum model degree in local region. The practical case is studied to refine the local quasi-geoid model with SCH using GPS/leveling data at 85 points. The results indicate that the local quasi-geoid model can reach 3 centimeter-level at the internal and external fitting precision.

scholarly journals An Improved BP Neural Network Algorithm for Prediction of Roadway Support

2021 ◽  
Vol 15 ◽  
pp. 393-399
Author(s):  
Yan-Jun He ◽  
Jin-shan Zhang ◽  
Chao-Gang Pan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Engineering Practice ◽  
Related Factors ◽  
Support Design ◽  
Network Training ◽  
Coal Roadway ◽  
Allowable Range ◽  
Roadway Support ◽  
New Research

Based on the engineering practice and the research and analysis of the knowledge in the field of roadway support, the paper puts forward to use an improved BP neural network to study the supporting types by the investigation, and obtained the related factors of the supporting types of the mining roadway and the successful reinforcement cases of the roadway. The proposed algorithm is applied to the prediction of coal roadway support parameters, and the main influencing factors of coal roadway support design are determined. From the typical engineering cases of roadway support collected on site as neural network training samples, the forecasting model of support parameters is established. Through the experimental data and simulation results, it can be seen that both the error convergence process and results of convergence speed, convergence accuracy and support types are ideal, the prediction error is within the allowable range, and the prediction accuracy is high, which verifies the reliability of this method and provides a new research idea and good application value for the study of support types of mining roadway.

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