Study on tunnel settlement prediction method based on parallel grey neural network model

2015 ◽  
Author(s):  
Lei Zhu ◽  
Teng Huang ◽  
Yue-qian Shen ◽  
Xian-min Zeng
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Method ◽  
Settlement Prediction

Optimization and Analysis for Coke Consumption Based on Clustering

2011 ◽  
Vol 287-290 ◽  
pp. 1112-1115
Author(s):  
Jun Hong Zhang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Clustering Analysis ◽  
Coke Consumption ◽  
Prediction Method ◽  
Absolute Error ◽  
Coke Rate ◽  
The Neural Network ◽  
Fuel Rate

In order to reduce the coke consumption of Blast Furnace（BF），a relevance analysis is carried out for operation parameters and fuel rate of BF，and a prediction method that is combining clustering analysis and artificial neural network for coke rate is proposed. The data cluster is divided into several classes by clustering analysis，the data similarity is high，and the neural network model is used to realize the prediction of coke rate. By combining the neural network with clustering analysis，the data in one BF is simulated，and the results are compared with the traditional neural network model. The result shows that the improved neural network has a higher accuracy, the average absolute error can be decreased by 3.13kg/t, and the average relative error can be decreased by 5.19%, it will have a good using foreground.

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 A prediction method of regional water resources carrying capacity based on artificial neural network

2021 ◽  
Vol 25 (2) ◽  
pp. 169-177
Author(s):  
Chaoyang Shi ◽  
Zhen Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Resources ◽  
Network Model ◽  
Neural Network Model ◽  
Carrying Capacity ◽  
Prediction Method ◽  
Water Resources Carrying Capacity ◽  
Regional Water ◽  
Capacity Prediction

To better predict the water resources carrying capacity and guide the social and economic activities, a prediction method of regional water resources carrying capacity is proposed based on an artificial neural network. Zhaozhou County is selected as the research area of water resources carrying capacity prediction, and its natural geographical characteristics, social economy, and water resources situation are explored. According to the regional water resources quantity and utilization characteristics and evaluation emphasis, the evaluation index system of water resources carrying capacity is constructed to evaluate the importance and correlation of water resource carrying capacity. The pressure degree of water resources carrying capacity is divided into five grades. According to the evaluation standard of bearing capacity, the artificial intelligence BP neural network model is constructed. Based on the main impact factors of water resources carrying capacity in this area, the water resources carrying capacity grade is obtained by weight calculation and convergence iteration by using neural network model and influence factor data to realize the prediction of water resources carrying capacity. The research results show that the network model can meet the demand for precision. The prediction results have a high degree of fit with the actual data, indicating that human intelligence can obtain accurate prediction results in water resources carrying capacity prediction.

Settlement Prediction of Buildings Based on BP Neural Network

2014 ◽  
Vol 602-605 ◽  
pp. 3232-3234
Author(s):  
Jian Cheng Li
Keyword(s):  
Neural Network ◽  
Seismic Performance ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Existing Buildings ◽  
Large Area ◽  
Settlement Prediction ◽  
Prediction Result ◽  
Study Results

At present, the traditional neural network model have been used in settlement prediction of buildings area, but there are some limitations. In this paper, BP neural network is applied in the settlement prediction of buildings and the prediction result is compared with the measured values. The results show that: the use of BP neural network to predict the settlement of existing buildings is feasible. The study results can provide a reference for the anti-seismic performance census of existing large area buildings.

scholarly journals Runoff Prediction Method Based on Adaptive Elman Neural Network

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1113 ◽  
Author(s):  
Chenming Li ◽  
Lei Zhu ◽  
Zhiyao He ◽  
Hongmin Gao ◽  
Yao Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Method ◽  
Elman Neural Network ◽  
Runoff Forecasting ◽  
Runoff Prediction ◽  
Screening Factor

The prediction of medium- and long-term runoff is of great significance to the comprehensive utilization of water resources. Building an adaptive data-driven runoff prediction model by automatic identification of multivariate time series change in runoff forecasting and identifying its influence degree is an attractive and intricate task. At present, the commonly used screening factor method is correlational analysis; others offer multi-collinearity. If these factors are directly input into the model, the parameters of the model tend to increase, and the excessive redundancy and noise adversely affects the prediction results of the model. On the basis of previous studies on medium- and long-term runoff prediction methods, this paper proposes an Elman Neural Network (ENN) adaptive runoff prediction method based on normalized mutual information (NMI) and kernel principal component analysis (KPCA). In this method, the features of the screening factors are extracted automatically by using the mutual information automatic screening factor, and then input into the Elman Neural Network for training. With less features, the parameters of the Elman Neural Network model can be reduced, and the problem of overfitting of the Elman Neural Network model is effectively alleviated. The method is evaluated by using the annual average runoff data of Jinping hydropower station in Chengdu, China, from 2007 to 2011. The maximum relative error of multiple forecasts was found to be less than 16%, and forecast effect was good. The accuracy of prediction is further improved by averaging the results of multiple forecasts.

A Method of Network Security Situation Prediction Based on Gray Neural Network Model

2011 ◽  
Vol 63-64 ◽  
pp. 936-939 ◽  
Author(s):  
Nian Liu ◽  
Geng Li ◽  
Yong Liu
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Network Model ◽  
Neural Network Model ◽  
Evaluation System ◽  
Prediction Method ◽  
Response Strategy ◽  
Network Information ◽  
Intelligent Analysis ◽  
Bp Neural Network Model

In this paper, a new network security situation intelligent analysis prediction method is proposed, which applies GM(1,1) model and BP neural network model in the analytic prediction field of network security situation information, and combination and optimization is performed to it to improve the accuracy of network security situation prediction. By analyzing and calculating the great amount of information acquired from network security situation evaluation system, it is able to make prediction on the current security situation of network system and the its future change trend, and make and implement relative response strategy according to prediction results, and reduce the harm from network attacks and improve the emergency response ability of network information system, so that we can make preparation before great damage occurs and reduce or avoid any possible attack to ensure the smooth running of system. The experiment results show that this method is a better solution for network security situation prediction.

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