Based on the wavelet neural network analysis and forecast of deformation monitoring data

Conglin Zhou; Shihua Tang; Changzeng Tang; Qing Huang; Yintao Liu; Xinying Zhong; Feida Li; Hongwei Xu

doi:10.1117/12.2207604

9 December 2015 Based on the wavelet neural network analysis and forecast of deformation monitoring data

Conglin Zhou, Shihua Tang, Changzeng Tang, Qing Huang, Yintao Liu, Xinying Zhong, Feida Li, Hongwei Xu

Proceedings Volume 9808, International Conference on Intelligent Earth Observing and Applications 2015; 98081E (2015) https://doi.org/10.1117/12.2207604
Event: International Conference on Intelligent Earth Observing and Applications, 2015, Guilin, China

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.

Citation Download Citation

Conglin Zhou, Shihua Tang, Changzeng Tang, Qing Huang, Yintao Liu, Xinying Zhong, Feida Li, and Hongwei Xu "Based on the wavelet neural network analysis and forecast of deformation monitoring data", Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98081E (9 December 2015); https://doi.org/10.1117/12.2207604

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available