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Comprehensive Research on Intellective Deformation Analysis Method

Author: DengYong
Tutor: LiZongChun
School: PLA Information Engineering University
Course: Geodesy and Survey Engineering
Keywords: Deformation measurement Deformation prediction Wavelet Transform Artificial Neural Networks Wavelet Neural Network Genetic Wavelet Neural Network
CLC: P209
Type: Master's thesis
Year: 2010
Downloads: 40
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Abstract


With the continuous development of science and computer processing technology , the deformation measurement data are emerging to deal with the new method , artificial intelligence methods become the deformed data processing research hotspot . Compared with the traditional data processing methods , artificial intelligence methods is its self- learning , adaptive, fault-tolerant , and the forecast is accurate , etc. , plays an increasingly important role in the deformation analysis and accurate forecasting . In this paper, the characteristics of the artificial intelligence data processing method , combined on a variety of smart method for deformation analysis and prediction analysis study , the main contents are as follows : 1 , wavelet transform data denoising . Method using wavelet decomposition and reconstruction of the original observations the denoising data signal variance the four evaluation index changes in the amount of cross-correlation coefficient , signal-to-noise ratio and smoothness , and normalized to [0 , 1] , a comprehensive analysis of the method to determine the number of levels of the wavelet decomposition and reconstruction . 2 , wavelet neural network for deformation prediction accuracy . Easy to fall into local minima and slow convergence problem based on neural network exists , wavelet function instead of the activation function of the neural network to form the wavelet neural network , and deformation forecast , more precision than artificial neural network forecasting value . 3 wavelet neural network genetic algorithm applied to the prediction of deformation . Sometimes there would still be based on wavelet neural network slow convergence and the disadvantage of local extreme impact , use of genetic algorithms in global scope for the characteristics of the optimal solution , genetic algorithm and wavelet neural network combined with a genetic wavelet neural network , and successfully applied to the prediction of deformation , deformation forecasting more accurate than BP neural network and wavelet neural network . 4 to predict the stability and the effective range of the algorithm as the algorithm to judge the merits of the two indicators , analysis of BP neural network , the stability and the effective range of the wavelet neural network and genetic wavelet neural network three algorithms deformation prediction , further affirmed genetic wavelet neural network predictive ability . 5 , the analysis of several issues need to be addressed to build a multi - sensor deformation measurement system : the interaction of a variety of sensors and system data , analyze the problem of data storage management and data integration .

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CLC: > Astronomy,Earth Sciences > Surveying and Mapping > General issues > The application of computer
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