Neural network for optimal steiner tree computation

Chotipat Pornavalai, Norio Shiratori, Goutam Chakraborty

    Research output: Contribution to journalArticle

    2 Citations (Scopus)

    Abstract

    Hopfield neural network model for finding the shortest path between two nodes in a graph was proposed recently in some literatures. In this paper, we present a modified version of Hopfield model to a more general problem of searching an optimal tree (least total cost tree) from a source node to a number of destination nodes in a graph. This problem is called Steiner tree in graph theory, where it is proved to be a NP-complete. Through computer simulations, it is shown that the proposed model could always find an optimal or near-optimal valid solution in various graphs.

    Original languageEnglish
    Pages (from-to)139-149
    Number of pages11
    JournalNeural Processing Letters
    Volume3
    Issue number3
    Publication statusPublished - 1996

    Keywords

    • Hopfield model
    • Neural networks
    • Optimization
    • Steiner tree

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Neuroscience(all)

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  • Cite this

    Pornavalai, C., Shiratori, N., & Chakraborty, G. (1996). Neural network for optimal steiner tree computation. Neural Processing Letters, 3(3), 139-149.