Using a Stochastic Funnel to find NLR Starting Values

Adrian O'Connor, Junbin Gao, John Louis

    Research output: Book chapter/Published conference paperConference paperpeer-review

    1 Citation (Scopus)
    102 Downloads (Pure)


    This presentation introduces a new evolutionary algorithm that can be used to find starting values (SVs)for nonlinear regression (NLR) problems. The method is called a Stochastic Funnel Algorithm (SFA). The SFA is introduced through its operation in the SSE/parameter space of a NLR model. The SFA uses a relaxed trust region in the log10 space of the parameters of an NLR model. The SFA takes explicit advantage of the general shape of the Sum-of-Squares Error (SSE) of a data/model set to explore and exploit that space and eventually converge to the region of the best parameter estimates.
    Original languageEnglish
    Title of host publicationWORLDCOMP'08
    Subtitle of host publicationWorld Congress in Computer Science, Computer Engineering and Applied Computing
    EditorsHamid R Arabnia, Youngsong Mun
    Place of PublicationUSA
    PublisherCSREA Press
    Number of pages7
    ISBN (Electronic)1601320698
    Publication statusPublished - 2008
    EventInternational Conference on Genetic and Evolutionary Methods (GEM) - Las Vegas, Nevada, USA, New Zealand
    Duration: 14 Jul 200817 Jul 2008


    ConferenceInternational Conference on Genetic and Evolutionary Methods (GEM)
    Country/TerritoryNew Zealand


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