Determination of the complexity fitted model structure of Radial Basis Function Neural Networks

One of the disadvantages of using Artificial Neural Networks (ANNs) is their significant training time need, which scales with the complexity of the network and with the complexity of the problem that is needed to be solved. Radial Basis Function Neural Networks (RBFNNs) are neural networks that use the linear combination of radial basis functions, utilizing hybrid learning procedures which can solve the time requirement problem efficiently. However, it is not trivial to determine their structural parameters, such as the number of neurons as well as the parameters of each neuron. To solve that problem we have developed a new training method: we apply a clustering step to the training data, which results in information both about the quasi-optimum number of necessary neurons in the model and the approximate parameters of the neurons.