MMU Home | Prospectus | About MMU | A-Z Index | Contacts 
 


mmuch more
 
Search:
bullet
Browse
Collection All
bullet
bullet
bullet
bullet
bullet
Listed communities
bullet
bullet
bullet
bullet

espace at MMU > Faculties > Faculty of Science and Engineering > Department of Computing, Mathematics & Digital Technology > A global-local artificial neural network with application to wave overtopping prediction

Please use this identifier to cite or link to this item: http://hdl.handle.net/2173/31954
    Del.icio.us     LinkedIn     Citeulike     Connotea     Facebook     Stumble it!

SFX Query

Title: A global-local artificial neural network with application to wave overtopping prediction
Authors: Wedge, David C.
Ingram, David M.
McLean, David A.
Mingham, Clive G.
Bandar, Zuhair A.
Citation: Wedge, D.C. et al. A global-local artificial neural network with application to wave overtopping prediction. In Artificial Neural Networks: Formal Models and Their Applications - ICANN 2005, Springer, 2005, pp. 109-114
Publisher: Springer
Issue Date: 2005
URI: http://hdl.handle.net/2173/31954
DOI: 10.1007/11550907
Additional Links: http://www.springerlink.com/content/105633/
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
Type: Book chapter
Language: en
Description: The original publication is available at http://www.springer.com/
ISSN: 0302-9743
EISSN: 1611-3349
Appears in Collections: Department of Computing, Mathematics & Digital Technology
Department of Computing and Mathematics: Centre for Mathematical Modelling and Flow Analysis (CMMFA)
Environmental Science

Files in This Item:

There are no files associated with this item.



All Items in e-space are protected by copyright, with all rights reserved, unless otherwise indicated.

 

OR Logo Powered by Open Repository | Cookies
Valid XHTML 1.0!