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Proceedings Paper

Based on the wavelet neural network analysis and forecast of deformation monitoring data
Author(s): Conglin Zhou; Shihua Tang; Changzeng Tang; Qing Huang; Yintao Liu; Xinying Zhong; Feida Li; Hongwei Xu
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Paper Abstract

Combines the wavelet analysis and neural network, this paper will be processed the data and the traditional BP neural network and kalman filter are analyzed and compared. First of all to obtain data of dam deformation wavelet denoising, excluding the contaminated data, obtain the optimal data set. Threshold denoising is generally adopted. Then based on the BP neural network, wavelet analysis to improve the traditional neural network model. Improve the underlying layer upon layer number and the number of nodes. Combined with the optimized dam deformation data, using the improved network model, the results to the regression model, ordinary kalman filter, this paper compares and analyzes the prediction effect evaluation.Comparison result is more ideal, which indicates that the combination of wavelet neural network model for deformation data processing has a good precision.

Paper Details

Date Published: 9 December 2015
PDF: 8 pages
Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98081E (9 December 2015); doi: 10.1117/12.2207604
Show Author Affiliations
Conglin Zhou, Guilin Univ. of Technology (China)
Shihua Tang, Guilin Univ. of Technology (China)
Changzeng Tang, Geomatics Ctr. of Guangxi (China)
Qing Huang, Guilin Univ. of Technology (China)
Yintao Liu, Guilin Univ. of Technology (China)
Xinying Zhong, Geomatics Ctr. of Guangxi (China)
Feida Li, Guilin Univ. of Technology (China)
Hongwei Xu, Guilin Univ. of Technology (China)


Published in SPIE Proceedings Vol. 9808:
International Conference on Intelligent Earth Observing and Applications 2015
Guoqing Zhou; Chuanli Kang, Editor(s)

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