Third M.I.T. Conference on Computational Fluid and Solid Mechanics June 14–17, 2005  

Neural prediction of response spectra from mining tremors using recurrent layered networks and Kalman filtering

A. Krok*, Z. Waszczyszyn
Cracow University of Technology, Institute of Computer Methods in Civil Eng., Warszawska 24, 31-155 Krakow, Poland

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Acceleration response spectra (ARS) for mining tremors in the Upper Silesian Coalfield are generated using artificial neural networks trained by means of Kalman filtering. The target ARS were computed on the basis of measured accelerograms. It was proved that the recurrent layered network, trained by the recurrent decoupled extended kalman filter (RDEFK) algorithm is numerically much more efficient than the standard feed-forward NN learnt by DEKF. It is also shown that the considered KF algorithms are better than the traditional Rprop learning method.

Keywords:  Acceleration response spectrum; Mining tremor; Reccurent layered neural network; Kalman filtering

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