A global-local artificial neural network with application to wave overtopping prediction

D. C. Wedge, David Ingram, C. G. Mingham, D. McLean, Z. A. Bandar

Research output: Chapter in Book/Report/Conference proceedingChapter

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.
Original languageEnglish
Title of host publicationArtificial Neural Networks: Formal Models and Their Applications – ICANN 2005
Subtitle of host publication15th International Conference, Warsaw, Poland, September 11-15, 2005. Proceedings, Part II
PublisherSpringer-Verlag GmbH
Pages109-114
Number of pages6
Volume3697
ISBN (Electronic)978-3-540-28756-8
ISBN (Print)978-3-540-28755-1
DOIs
Publication statusPublished - 2005

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume3697
ISSN (Print)0302-9743

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