e-space
Manchester Metropolitan University's Research Repository

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

    Wedge, David C., Ingram, David M., McLean, David A., Mingham, Clive G. and Bandar, Zuhair A. (2005) A global-local artificial neural network with application to wave overtopping prediction. In: 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. Springer.

    File not available for download.

    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

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    0Downloads
    6 month trend
    517Hits

    Additional statistics for this dataset are available via IRStats2.

    Repository staff only

    Edit record Edit record