A Global-Local Artificial Neural Network with application to wave overtopping prediction

David Wedge, David Ingram, David McLean, Clive Mingham, Zuhair Bandar

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We present a hybrid Radial Basis Function (RBF) - sigmoid neural network with a three-step training algorithm that utilises both global search and gradient descent training. We test the effectiveness of our method using four synthetic datasets and demonstrate its use in wave overtopping prediction. It is shown that the hybrid architecture is often superior to architectures containing neurons of a single type in several ways: lower errors are often achievable using fewer hidden neurons and with less need for regularisation. Our Global-Local Artificial Neural Network (GL-ANN) is also seen to compare favourably with both Perceptron Radial Basis Net (PRBFN) and Regression Tree RBFs. © Springer-Verlag Berlin Heidelberg 2005.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    PublisherSpringer Nature
    Pages109-114
    Number of pages5
    Volume3697
    ISBN (Print)3540287558, 9783540287551
    Publication statusPublished - 2005
    Event15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005 - Warsaw
    Duration: 1 Jul 2005 → …
    http://dblp.uni-trier.de/db/conf/icann/icann2005-1.html#BoseFS05http://dblp.uni-trier.de/rec/bibtex/conf/icann/BoseFS05.xmlhttp://dblp.uni-trier.de/rec/bibtex/conf/icann/BoseFS05

    Conference

    Conference15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005
    CityWarsaw
    Period1/07/05 → …
    Internet address

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