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New method of generating spectrum compatible accelerograms using neural networks
Author(s) -
Ghaboussi Jamshid,
Lin ChuChieh J.
Publication year - 1998
Publication title -
earthquake engineering and structural dynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.218
H-Index - 127
eISSN - 1096-9845
pISSN - 0098-8847
DOI - 10.1002/(sici)1096-9845(199804)27:4<377::aid-eqe735>3.0.co;2-2
Subject(s) - artificial neural network , computer science , algorithm , fourier transform , spectrum (functional analysis) , inverse , simple (philosophy) , dimension (graph theory) , response spectrum , artificial intelligence , pattern recognition (psychology) , engineering , structural engineering , mathematics , geometry , mathematical analysis , physics , quantum mechanics , philosophy , epistemology , pure mathematics
A new method is proposed for generating artificial earthquake accelerograms from response spectra. This method uses the learning capabilities of neural networks to developed the knowledge of the inverse mapping from the response spectra to earthquake accelerogram. In the proposed method the neural networks learn the inverse mapping directly from the actual recorded earthquake accelerograms and their response spectra. A two‐stage approach is used. In the first stage, a replicator neural network is used as a data compression tool. The replicator neural network compresses the vector of the discrete Fourier spectra of the accelerograms to vectors of much smaller dimension. In the second stage, a multi‐layer feed‐forward neural network learns to relate the response spectrum to the compressed Fourier spectrum. A simple example is presented, in which only 30 accelerograms are used to train the two‐stage neural networks. This example demonstrates how the method works and shows its potential. © 1998 John Wiley & Sons, Ltd.