The Hazard Assessment of Karst Surface Collapse Risk Zoning Based on BP Neural Network in Wuhan City

2013 ◽  
Vol 405-408 ◽  
pp. 2376-2379
Author(s):  
Zhi Gang Li ◽  
Shang De Xiao ◽  
Yi Heng Pan ◽  
Shi Wei Lu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Karst Collapse ◽  
Steel Mill ◽  
Wuhan City ◽  
Risk Zoning ◽  
Set Up ◽  
Neural Network Theory ◽  
Surface Collapse

One of the main geological disasters in Wuhan City is karst surface collapse. Analytically the main elements affecting karst collapse contain Karst development, covering layer condition and hydrogeological condition. This paper aims to set up the risk zoning evaluation model about this disaster upon BP neural network theory. And then evaluate the risk zoning of karst collapse. The assessment result shows karst surface collapse of high risk in Wuhan City mainly distributes in Ruanjia Lane, Lujia Street, Justice School, Fenghuo Village,Zhongnan Steel Mill and Maotan Harbor.

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.

Study on Intelligent Analysis of the Causes of SMT Solder Joint Defects Based on Fuzzy Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 3257-3261
Author(s):  
Chun Yue Huang ◽  
He Geng Wei ◽  
Tian Ming Li ◽  
De Jin Yan
Keyword(s):  
Neural Network ◽  
Solder Joint ◽  
Membership Function ◽  
Fuzzy Neural Network ◽  
Evaluation Model ◽  
Training Samples ◽  
Intelligent Analysis ◽  
Fuzzy Neural ◽  
Input Variables ◽  
Set Up

By determining membership function of the input parameters and selecting defuzzification method, the evaluation model which can be used to intelligent analyzing the causes of SMT solder joint defects was set up. The fuzzy neural network was trained by using the output variables of the training samples from intelligent discrimination as the input variables of training samples of fuzzy neural network. The fuzzy neural network was tested by using the output variables of the testing samples from intelligent discrimination as the input variables of testing samples of fuzzy neural network. The results show that by using the evaluation model the cause of SMT solder joint defects can be analyzed intelligently and the results of intelligently analysis are reasonable, the evaluation model can be used practically.

Geological Adaptive Cutterhead Selection for EPB Shield Based on BP Neural Network

2014 ◽  
Vol 607 ◽  
pp. 118-123
Author(s):  
Lai Kuang Lin ◽  
Yi Min Xia ◽  
Fei He ◽  
Qing Song Mao ◽  
Kui Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Earth Pressure ◽  
Training Model ◽  
Structure Design ◽  
Pressure Balance ◽  
Selection For ◽  
Earth Pressure Balance ◽  
Set Up ◽  
Epb Shield

In view of complex and fuzziness of geological adaptive cutterhead selection for earth pressure balance (EPB) shield, a cutterhead selection method based on BP neural network is put forward. Considering the structure characteristics of EPB shield cutterhead, typical cutterhead types are classified and summarized based on cutterhead topology structure and number of spokes. After analyzing the determinants of cutterhead selection, one-to-many mapping relation between cutterhead type and geological parameters is put forward, and then core geologic parameters related to cutterhead selection are concluded. The feasibility of using neural network method to choose the cutterhead type is analyzed, and a BP neural network training model for cutterhead selection is set up and tested in testing sample data. The result shows that the selected cutterhead and the construction cutterhead are basically consistent. The feasibility of this method is proved and it can be theoretical basis for the cutterhead structure design which will improve scientific of cutterhead selection.

A RBF Neural Network Approach for Fitting Creep Curve of Sandstone

2010 ◽  
Vol 171-172 ◽  
pp. 274-277
Author(s):  
Yun Liang Tan ◽  
Ze Zhang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Creep Curve ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Reference Value ◽  
Network Approach ◽  
Neural Network Approach ◽  
Training Time ◽  
Set Up

In order to quest an effective approach for predicate the rheologic deformation of sandstone based on some experimental data, an improved approaching model of RBF neural network was set up. The results show, the training time of improved RBF neural network is only about 10 percent of that of the BP neural network; the improved RBF neural network has a high predicating accuracy, the average relative predication error is only 7.9%. It has a reference value for the similar rock mechanics problem.

scholarly journals Optimization of Online Teaching Quality Evaluation Model Based on Hierarchical PSO-BP Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Luxin Jiang ◽  
Xiaohui Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Online Teaching ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Particle Swarm ◽  
Teaching Quality ◽  
Particle Swarm Algorithm ◽  
Bp Model ◽  
Quality Evaluation Model

In the evaluation of teaching quality, aiming at the shortcomings of slow convergence of BP neural network and easy to fall into local optimum, an online teaching quality evaluation model based on analytic hierarchy process (AHP) and particle swarm optimization BP neural network (PSO-BP) is proposed. Firstly, an online teaching quality evaluation system was established by using the analytic hierarchy process to determine the weight of each subsystem and each index in the online teaching quality evaluation system and then combined with actual experience, the risk value of each index was constructed according to safety regulations. The regression model is established through BP neural network, and the weight and threshold of the model are optimized by the particle swarm algorithm. Based on the online teaching quality evaluation model of BP neural network, the parameters of the model are constantly adjusted, the appropriate function is selected, and the particle swarm algorithm which is used in the training and learning process of the neural network is optimized. The scientificity of the questionnaire was verified by reliability and validity test. According to the scoring results and combined with the weight coefficient of each indicator in the online course quality evaluation index system, the key factors affecting the quality of online courses were obtained. Based on the survey data, descriptive statistics, analysis of variance, and Pearson’s correlation coefficient method are used to verify the research hypothesis and obtain valuable empirical results. By comparing the model with the standard BP model, the results show that the accuracy of the PSO-BP model is higher than that of the standard BP model and PSO-BP effectively overcomes the shortcomings of the BP neural network.

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