Earth-printshttps://www.earth-prints.orgThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Thu, 12 Sep 2024 09:04:36 GMT2024-09-12T09:04:36Z5011Modeling volcanomagnetic dynamics by recurrent least-squares support vector machineshttp://hdl.handle.net/2122/6376Title: Modeling volcanomagnetic dynamics by recurrent least-squares support vector machines
Authors: Jankowski, S.; Currenti, G.; Napoli, R.; Szymanski1, Z.; Fortuna, L.; Del Negro, C.
Abstract: Nonlinear dynamic systems can be described by
means of statistical learning theory: neural
networks and kernel machines. In this work the
recurrent least-squares support vector machines
are chosen as learning system. The unknown
dynamic system is a mapping of past states into
the future. The recurrent system is implemented
by special data preparation in the learning phase.
The next iterations can be calculated but the
convergence is usually not guaranteed. Due to the
fact that the predicted trajectory can diverge from
the real trajectory the semi-directed mode can be
applied, i.e. after several prediction steps the
system is updated by using the current values of
the considered process as new initial conditions.
The idea was tested on the data generated by the
chaotic dynamic system – the Chua’s circuit. The
methodology was then applied to real magnetic
data acquired at Etna volcano.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/2122/63762010-01-01T00:00:00Z