The radial basis function (RBF) network has its foundation in the conventional approximation
theory. It has the capability of universal approximation. The RBF network is a popular …

An incremental network model is introduced which is able to learn the important topological
relations in a given set of input vectors by means of a simple Hebb-like learning rule. In …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system. Neural networks are a model-free …

Subset selection and regularization are two well-known techniques that can improve the
generalization performance of nonparametric linear regression estimators, such as radial …

Over the past, deep neural networks have proved to be an essential element for develo**
intelligent solutions. They have achieved remarkable performances at a cost of deeper …

The Handbook of Neural Computation is a practical, hands-on guide to the design and
implementation of neural networks used by scientists and engineers to tackle difficult and/or …

We present a new algorithm for the construction of radial basis function (RBF) networks. The
method uses accumulated error information to determine where to insert new units. The …

Suppose we want to use an intelligent agent (computer program or robot) for performing
tasks for us, but we cannot or do not want to specify the precise task-operations. Eg we may …

Successful implementations of radial-basis function (RBF) networks for classification tasks
must deal with architectural issues, the burden of irrelevant attributes, scaling, and some …

This article is concerned with the optimal use of metamodels in the context of multi-objective
evolutionary algorithms which are based on computationally expensive function evaluations …