Neural Computing & Applications

, Volume 13, Issue 1, pp 24–31 | Cite as

A comparison between functional networks and artificial neural networks for the prediction of fishing catches

  • Alfonso Iglesias
  • Bernardino Arcay
  • J. M. Cotos
  • J. A. Taboada
  • Carlos Dafonte
Original Article


In recent years, functional networks have emerged as an extension of artificial neural networks (ANNs). In this article, we apply both network techniques to predict the catches of the Prionace Glauca (a class of shark) and the Katsowonus Pelamis (a variety of tuna, more commonly known as the Skipjack). We have developed an application that will help reduce the search time for good fishing zones and thereby increase the fleet’s competitivity. Our results show that, thanks to their superior learning and generalisation capacities, functional networks are more efficient than ANNs. Our data proceeds from remote sensors. Their spectral signatures allow us to calculate products that are useful for ecological modelling. After an initial phase of digital image processing, we created a database that provides all the necessary patterns to train both network types.


Artificial neural network (ANN) Functional network Remote sensing 


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Copyright information

© Springer-Verlag London Limited 2004

Authors and Affiliations

  • Alfonso Iglesias
    • 1
  • Bernardino Arcay
    • 1
  • J. M. Cotos
    • 2
  • J. A. Taboada
    • 2
  • Carlos Dafonte
    • 1
  1. 1.Dept. of Information and Communications TechnologiesUniversity of A CorunaSpain
  2. 2.Remote sensing laboratory (TELSIG), Dept. of Electronic and ComputationUniversity of SantiagoSpain

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