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dc.contributor.authorBarrile, Vincenzo-
dc.contributor.authorCacciola, Matteo-
dc.contributor.authorD'Amico, Sebastiano-
dc.contributor.authorGreco, Antonino-
dc.contributor.authorMorabito, Francesco Carlo-
dc.contributor.authorParrillo, Francesco-
dc.date.accessioned2022-06-04T09:35:50Z-
dc.date.available2022-06-04T09:35:50Z-
dc.date.issued2006-10-
dc.identifier.citationBarrile, V., Cacciola, M., D’Amico, S., Greco, A., Morabito, F., & Parrillo, F. (2006). Radial basis function neural networks to foresee aftershocks in seismic sequences related to large earthquakes. ICONIP 2006, Hong Kong (pp. 909-916).en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar/handle/123456789/97111-
dc.description.abstractRadial Basis Function Neural Network are known in scientific literature for their abilities in function approximation. Above all, this particular kind of Artificial Neural Network is applied to time series forecasting in non-linear problems, where estimation of future samples starting from already detected quantities is very hardly. In this paper Radial Basis Function Neural Network was implemented in order to predict the trend of n(t) for aftershocks temporal series, that is the numerical series of daily-earthquake’s number occurred after a great earthquake with magnitude M > 7.0 Richter. In particular we implemented the RBF-NN for the Colfiorito seismic sequence. The seismic sequences considered in this work are obtained following criteria already known in scientific literature. Results of proposed approach are very encouraging.en_GB
dc.language.isoenen_GB
dc.publisherSpringer-Verlagen_GB
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_GB
dc.subjectRadial basis functionsen_GB
dc.subjectNeural networks (Computer science)en_GB
dc.subjectEarthquake aftershocksen_GB
dc.subjectEarthquake prediction -- Data processingen_GB
dc.titleRadial basis function neural networks to foresee aftershocks in seismic sequences related to large earthquakesen_GB
dc.typeconferenceObjecten_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.bibliographicCitation.conferencenameICONIP 2006en_GB
dc.bibliographicCitation.conferenceplaceHong Kong, China, 3-6/10/2006en_GB
dc.description.reviewedpeer-revieweden_GB
dc.identifier.doi10.1007/11893257_100-
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