Radial basis functions (RBFs) consist of a two-layer neural network, where each hidden unit implements a kernel function. Each kernel is associated with an activation region from the input space and its output is fed to an output unit. In order to find the parameters of a neural network which embeds this structure we take into consideration two different statistical approaches. The first approa...

In studies concerned with sustainability the underlying models are, in most cases, not strictly numerical since they depend on many conditions that can be regarded as qualitative. In this paper, a model to evaluate citizens satisfaction learnt from data collected from a survey is presented. The model, which involves the use of RBF neural networks, will provide local councillors with useful info...

| Successful implementations of radial-basis function (RBF) networks for classiication tasks must deal with architectural issues, the burden of irrelevant attributes, scaling , and some other problems. This paper addresses these issues by initializing RBF networks with decision trees that deene relatively pure regions in the instance space; each of these regions then determines one basis functi...

Neural Networks have become very useful tools for input-output knowledge discovery. However, some of the most powerful schemes require very complex machines, and thus a large amount of calculation. This paper presents a general technique to reduce the computational burden associated to the operational phase of most neural networks that calculate their output as a weighted sum of terms, which co...

It is very important to evaluate water quality in environment protection. Water environment is a complicated system, traditional methods cannot meet the demands of water environment protection. In view of the deficiency of the traditional methods, a BP neural network model and a RBF neural network model are proposed to evaluate water quality. The proposed model was applied to evaluate the water...

In this paper the neural network based lter for nonlinear interference cancellation is developed. The Hyper Radial Basis Function (HRBF) network with associated Manhattan learning algorithm is proposed for non-linear noise cancellation under assumption that reference noise is available. The HRBF network is a generalization of radial basis function (RBF) and generalized radial basis function (GR...