This contribution is oriented towards proposing a novel strategy to simulate and predict planetary orbits based on exploiting the optimized performance of hypercomplex neural networks. These structures perform a learning process based on quaternion algebra thus leading to lower the number of epochs required to obtain an adequate degree of approximation. Moreover, we propose a strategy for predicting the outcome of the learning phase by instantiating suitable hypercomplex auxiliary networks to predict the trends of the main weights of the network.

Hypercomplex Multilayer Perceptron for Planetary Orbits Prediction

Buscarino A.;Famoso C.;Fortuna L.;Puglisi G.
2023-01-01

Abstract

This contribution is oriented towards proposing a novel strategy to simulate and predict planetary orbits based on exploiting the optimized performance of hypercomplex neural networks. These structures perform a learning process based on quaternion algebra thus leading to lower the number of epochs required to obtain an adequate degree of approximation. Moreover, we propose a strategy for predicting the outcome of the learning phase by instantiating suitable hypercomplex auxiliary networks to predict the trends of the main weights of the network.
2023
neural networks
orbital mechanics
parallelization
quaternions
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/619652
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact