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.
|Title of host publication||Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005|
|Subtitle of host publication||15th International Conference, Warsaw, Poland, September 11-15, 2005. Proceedings, Part II|
|Number of pages||6|
|Publication status||Published - 2005|
|Name||Lecture Notes in Computer Science|
|Publisher||Springer Berlin Heidelberg|