A Global-Local Artificial Neural Network with Application to Wave Overtopping Prediction

  • David Wedge
  • David Ingram
  • David McLean
  • Clive Mingham
  • Zuhair Bandar
Conference paper

DOI: 10.1007/11550907_18

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3697)
Cite this paper as:
Wedge D., Ingram D., McLean D., Mingham C., Bandar Z. (2005) A Global-Local Artificial Neural Network with Application to Wave Overtopping Prediction. In: Duch W., Kacprzyk J., Oja E., Zadrożny S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • David Wedge
    • 1
  • David Ingram
    • 1
  • David McLean
    • 1
  • Clive Mingham
    • 1
  • Zuhair Bandar
    • 1
  1. 1.Department of Computing and MathematicsManchester Metropolitan UniversityManchesterUnited Kingdom

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