Kalman filtering for neural prediction of response spectra from mining tremors

Acceleration response spectra (ARS) for mining tremors in the Upper Silesian Coalfield, Poland are generated using neural networks trained by means of Kalman filtering. The target ARS were computed on the base of measured accelerograms. It was proved that the standard feed-forward, layered neural ne...

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Bibliographic Details
Published in:Computers & structures 2007-08, Vol.85 (15), p.1257-1263
Main Authors: Krok, Agnieszka, Waszczyszyn, Zenon
Format: Article
Language:English
Subjects:
Acceleration response spectrum
Autoregressive input
Computational techniques
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
Kalman filtering
Mathematical methods in physics
Mining tremor
Neural networks
Physics
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Summary:Acceleration response spectra (ARS) for mining tremors in the Upper Silesian Coalfield, Poland are generated using neural networks trained by means of Kalman filtering. The target ARS were computed on the base of measured accelerograms. It was proved that the standard feed-forward, layered neural network, trained by the DEFK (decoupled extended Kalman filter) algorithm is numerically much less efficient than the standard recurrent NN learnt by Recurrent DEKF, cf. [Haykin S, (editor). Kalman filtering and neural networks. New York: John Wiley & Sons; 2001]. It is also shown that the studied KF algorithms are better than the traditional Resilient-Propagation learning method. The improvement of the training process and neural prediction due to introduction of an autoregressive input is also discussed in the paper.
ISSN:0045-7949
1879-2243
DOI:10.1016/j.compstruc.2006.11.029