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

  • Authors:

  • David Wedge;David Ingram;David McLean;Clive Mingham;Zuhair Bandar

  • Affiliations:

  • Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom;Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom;Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom;Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom;Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom

  • Venue:
  • ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
  • Year:
  • 2005

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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.