Skip to Main Content (Press Enter)

Logo CNR
  • ×
  • Home
  • Persone
  • Pubblicazioni
  • Strutture
  • Competenze

UNI-FIND
Logo CNR

|

UNI-FIND

cnr.it
  • ×
  • Home
  • Persone
  • Pubblicazioni
  • Strutture
  • Competenze
  1. Pubblicazioni

A Neural Network approach to estimate the magnitude of forthcoming earthquakes

Abstract
Data di Pubblicazione:
2004
Abstract:
A neural network is a highly connected system, whose elementary unit is the artificial neuron that reproduces in some way the nonlinear function of a biological neuron of the brain. Neuron has many input lines (dendrites) and only one output line (axon), which, by means of a connection (synapses), goes to another neuron or is taken as one output of the system. Using this approach we have developed a neural system to estimate the magnitude of forthcoming earthquakes. The neural system we use for the earthquake magnitude estimation problem is a multilayer backpropagation feedforward neural network. This is usually referred to as MLP (MultiLayer Perceptron). We have validated this estimation scheme to the sequence of earthquake magnitudes of some seismic areas of Italy. Our results reveal a good agrrement between the estimated with the observed magnitudes.
Tipologia CRIS:
01.05 Abstract in rivista
Keywords:
connected system; artificial
Elenco autori:
Viggiano, Mariassunta; Telesca, Luciano
Autori di Ateneo:
TELESCA LUCIANO
VIGGIANO MARIASSUNTA
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/97058
Pubblicato in:
GEOPHYSICAL RESEARCH ABSTRACTS (ONLINE)
Journal
  • Utilizzo dei cookie

Realizzato con VIVO | Designed by Cineca | 26.5.0.0 | Sorgente dati: PREPROD (Ribaltamento disabilitato)