In this paper we apply normalized radial basis function networks to function learning and in nonparametric classification. A simple parameter learning technique is proposed and convergence and the rates of convergence of the empirically trained networks are studied theoretically and in computer experiments.

In this article a new neural-network architecture suitable for learning and generalization is discussed and developed. Although similar to the radial basis function (RBF) net, our computational model called the net with complex weights (CWN) has demonstrated a considerable gain in performance and efficiency in number of applications compared to RBF net. Its better performance in classification ...

Abstract This paper proposes a local meshless radial basis function (RBF) method to obtain the solution of two-dimensional time-fractional Sobolev equation. The model is formulated with Caputo fractional derivative. uses RBF approximate spatial operator, and finite-difference algorithm as time-stepping approach for in time. stability technique examined by using matrix method. Finally, two numer...

In this paper we propose the idea of building a new software reliability models using Radial Basis Function (RBF) network. The RBF network is easy to design and the network structure can be represented in a simple mathematical equation. Our goal is to build a generalized model that can be used for software predication [1]. The RBF network was trained with a set of data collected from the testin...

Among the neural network models RBF(Radial Basis Function) network seems to be quite effective for a pattern recognition task such as handwritten numeral recognition since it is extremely flexible to accommodate various and minute variations in data. Recently we obtained a good recognition rate for handwritten numerals by using an RBF network. In this paper we show how to design an RBF network ...

This paper describes a global approach to the construction of Radial Basis Function (RBF) neural net classifier. We used a new simple algorithm to completely define the structure of the RBF classifier. This algorithm has the major advantage to require only the training set (no step learning, threshold or other parameters as in other methods). Tests on several benchmark datasets showed, despite ...