The Slope Deformation Forecast Model Based on Kalman Filter and Wavelet Neural Network
2013 ◽
Vol 671-674
◽
pp. 323-327
Keyword(s):
Deformation is the macroscopic index for the structure of geotechnical engineering, it is important for the design and construction of geotechnical engineering to monitor the deformation and analyze the monitored data. Kalman filter can enhance the effectiveness of the monitored data and wavelet neural network has the favorable time-frequency localization features and self-learning function. Firstly, the monitored data has been filtered by Kalman filter, and then a deformation forecast model will be established by means of combining with neural network wavelet to predict the deformation of actual engineering. The result shows that the forecast model is successful and effective to forecast the slope deformation.
2010 ◽
Vol 37-38
◽
pp. 1581-1584
2012 ◽
Vol 452-453
◽
pp. 782-788
2013 ◽
Vol 307
◽
pp. 327-330
2012 ◽
Vol 490-495
◽
pp. 623-627
Keyword(s):
Keyword(s):
2012 ◽
Vol 31
(6)
◽
pp. 1872-1891
◽
2013 ◽
Vol 765-767
◽
pp. 1019-1022
2014 ◽
Vol 915-916
◽
pp. 1532-1535