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    Computational aspects of the optimal transit path problem

    119234_Computational%20aspects-Journal%20of%20Ind%20Man%20Optim%20JIMO-AIMS.pdf (173.8Kb)
    Access Status
    Open access
    Authors
    Caccetta, Louis
    Loosen, Ian
    Rehbock, Volker
    Date
    2008
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Caccetta, Louis and Loosen, Ian and Rehbock, Volker. 2008. Computational aspects of the optimal transit path problem. Journal of Industrial and Management Optimization. 4 (1): pp. 95-105.
    Source Title
    Journal of Industrial and Management Optimization JIMO
    Additional URLs
    http://aimsciences.org/journals/jimo/contents.jsp
    ISSN
    15475816
    Faculty
    Department of Mathematics and Statistics
    School of Science
    Faculty of Science and Engineering
    Remarks

    This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Journal of Industrial and Management Optimization 'JIMO' following peer review. The definitive publisher-authenticated version: “Caccetta, Louis and Loosen, Ian and Rehbock, Volker. 2008. Computational aspects of the optimal transit path problem. Journal of Industrial and Management Optimization. Vol. 4 (1): pp. 95-105.” is available online at: <a href="http://aimsciences.org/journals/pdfs.jsp?paperID=3087&mode=full">http://aimsciences.org/journals/pdfs.jsp?paperID=3087&mode=full</a>

    URI
    http://hdl.handle.net/20.500.11937/3053
    Collection
    • Curtin Research Publications
    Abstract

    In this paper we present a novel method for short term forecast of time series based on Knot-Optimizing Spline Networks (KOSNETS). The time series is first approximated by a nonlinear recurrent system. The resulting recurrent system is then approximated by feedforward B-spline networks, yielding a nonlinear optimization problem. In this optimization problem, both the knot points and the coefficients of the B-splines are decision variables so that the solution to the problem has both optimal coefficients and partition points. To demonstrate the usefulness and accuracy of the method, numerical simulations and tests using various model and real time series are performed. The numerical simulation results are compared with those from a well-known regression method, MARS. The comparison shows that our method outperforms MARS for nonlinear problems.

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