Time Series Analysis of Slope Displacement Using Neural Network Method

2013 ◽  
Vol 405-408 ◽  
pp. 129-132
Author(s):  
Zhi Qiang Zhang ◽  
Yan Liang Wen ◽  
Guo Jian Zhang ◽  
Lai Shan Chang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Theory ◽  
System Analysis ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Network Method ◽  
Neural Network System ◽  
Neural Network Theory ◽  
Slope Displacement

Based on the artificial neural network theory, a neural network approach is proposed for the analysis of slope displacement time series, the neural network system analysis of slope displacement time series is developed, it is proved that this method is scientific and reasonable.

International Real Estate Review

2004 ◽  
Vol 7 (1) ◽  
pp. 121-138
Author(s):  
Xin J. Ge ◽  
◽  
G. Runeson ◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hong Kong ◽  
Time Series Data ◽  
Series Data ◽  
Residential Property ◽  
Neural Network Approach ◽  
Price Performance ◽  
The Neural Network ◽  
Property Price

This paper develops a forecasting model of residential property prices for Hong Kong using an artificial neural network approach. Quarterly time-series data are applied for testing and the empirical results suggest that property price index, lagged one period, rental index, and the number of agreements for sales and purchases of units are the major determinants of the residential property price performance in Hong Kong. The results also suggest that the neural network methodology has the ability to learn, generalize, and converge time series.

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.

scholarly journals ESTIMATION OF THE LYAPUNOV SPECTRUM FROM ONE-DIMENSIONAL OBSERVATIONS USING NEURAL NETWORKS

2014 ◽  
pp. 30-34
Author(s):  
Vladimir Golovko
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Neural Networks ◽  
Time Series ◽  
Lyapunov Spectrum ◽  
Network Approach ◽  
Neural Network Approach ◽  
One Dimensional ◽  
The Neural Network ◽  
Attractor Dynamics

This paper discusses the neural network approach for computing of Lyapunov spectrum using one dimensional time series from unknown dynamical system. Such an approach is based on the reconstruction of attractor dynamics and applying of multilayer perceptron (MLP) for forecasting the next state of dynamical system from the previous one. It allows for evaluating the Lyapunov spectrum of unknown dynamical system accurately and efficiently only by using one observation. The results of experiments are discussed.

Forecasting Emerging Market Indexes with Neural Networks

2011 ◽  
pp. 80-101 ◽  
Author(s):  
Steven Walczah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Global Knowledge ◽  
Index Change ◽  
Market Index ◽  
The Neural Network ◽  
Neural Network System ◽  
Homogeneous Models ◽  
Using Data

Forecasting financial time series with neural networks is problematic. Multiple decisions, each of which affects the performance of the neural network forecasting model, must be made, including which data to use and the size and architecture of the neural network system. While most previous research with neural networks has focused on homogenous models, that is, only using data from the single time series to be forecast, the ever more global nature of the world’s financial markets necessitates the inclusion of more global knowledge into neural network design. This chapter demonstrates how specific markets are at least partially dependent on other global markets and that inclusion of heterogeneous market information will improve neural network forecasting performance over similar homogeneous models by as much as 12 percent (i.e., moving from a near 51% prediction accuracy for the direction of the market index change to a 63% accuracy of predicting the direction of the market index change).

scholarly journals Calculation of Effective Structural Number Using Simple Neural Network for Some Road Links in Indonesia

2018 ◽  
Vol 14 (2) ◽  
pp. 45
Author(s):  
Siegfried Syafier
Keyword(s):  
Neural Network ◽  
System Analysis ◽  
Falling Weight Deflectometer ◽  
Acceptable Error ◽  
Pavement Maintenance ◽  
The Neural Network ◽  
Network Method ◽  
Simple Neural Network ◽  
Falling Weight ◽  
Layer Composition

In the pavement maintenance system, the parameter of effective structural number (SNeff) would be a considered factor in deciding whether a road link would be repaired or not. To calculate this parameter, it is required the testing of Falling Weight Deflectometer (FWD) and information of layer composition and thicknesses. The combination of these information and using the method of AASHTO’93, it can be calculated the SNeff. These two information generally would be gained through the testings of core drill and test pit which would take time and cost. To overcome these problems, the neural network method or precisely the artificial neural network is developed for analysis of pavement structure. From the analysis, it can be said that the neural network of single perceptron can be used for predicting the SNeff with an acceptable error. In general the value of SNeff obtained from neural network calculation is lower than that of AASHTO’93. In this paper it is also recommended to develop the neural network using multi layer perceptron for the use on pavement system analysis that might be decreasing the error.

Simulation of Failure Detection Based on Neural Network for No-Ball Mill

2011 ◽  
Vol 201-203 ◽  
pp. 627-631
Author(s):  
Kun Shan Li ◽  
Xin Hua Wang ◽  
Wen Ming Wang
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Network Theory ◽  
Ball Mill ◽  
Detection System ◽  
Structural Characteristics ◽  
Failure Detection ◽  
The Neural Network ◽  
Point Set ◽  
Neural Network Theory

According to the structural characteristics of non-ball mill, using the neural network theory to select and measure point, set the failure mode, analyze and determine the cause of malfunction. The newly developed fault detection system was used to simulative detect fault. Through data processing, the results can be directly derived which could be fed back into the design of non-ball mill, thereby improving the design.

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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