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International Work-Conference on Artificial Neural Networks

IWANN 2003: Computational Methods in Neural Modeling pp 342–349Cite as

Neural Implementation of Dijkstra’s Algorithm.

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2686))

Abstract

In this paper a new neural network model that generalizes Hopfleld model is proposed. It allows to implement an algorithm similar to the Dijkstra’s one in order to solve the shortest path problem on a weighted graph. With this neural and parallel implementation, the presented model is adapted to possible modifications in the graph. Moreover, it is possible to solve other related problems with the same structure.

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Author information

Authors and Affiliations

  1. Dept. Matematica Aplicada, Universidad de Málaga, Málaga

    E. Mérida-Casermeiro

  2. Dept. Ciencias de la Computation, Universidad de M’alaga, Málaga

    J. Muñoz-Pérez & R. Benítez-Rochel

Authors
  1. E. Mérida-Casermeiro
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  2. J. Muñoz-Pérez
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  3. R. Benítez-Rochel
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Editor information

Editors and Affiliations

  1. E.T.S. de Ingeniería Informática Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia, Juan del Rosal, 16, 28040, Madrid, Spain

    José Mira  & José R. Álvarez  & 

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© 2003 Springer-Verlag Berlin Heidelberg

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Mérida-Casermeiro, E., Muñoz-Pérez, J., Benítez-Rochel, R. (2003). Neural Implementation of Dijkstra’s Algorithm.. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_44

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  • DOI: https://doi.org/10.1007/3-540-44868-3_44

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40210-7

  • Online ISBN: 978-3-540-44868-6

  • eBook Packages: Springer Book Archive

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