This paper presents a new multiobjective evolutionary algorithm applied to a radial basis function (RBF) network design based on mult iobjective particle swarm optimization augmented with local search features. The algorithm is named the memetic multiobjective particle swarm optimization RBF network (MPSON) because it integrates the accuracy and structure of an RBF network. The proposed algorit...

This article presents a noticeable performances improvement of a neural classifier based on an RBF network. Based on the Mahalanobis distance, this new classifier increases relatively the recognition rate while decreasing remarkably the number of hidden layer neurons. We obtain thus a new very general RBF classifier, very simple, not requiring any adjustment parameter, and presenting an excelle...

This paper presents a novel technique which may be used to determine an appropriate threshold for interpreting the outputs of a trained Radial Basis Function (RBF) classifier. Results from two experiments demonstrate that this method can be used to improve the performance of RBF classifiers in practical applications.

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.

Metamodeling for High Dimensional Simulation-based Design Problems Songqing Shan Dept. of Mech. and Manuf. Engineering University of Manitoba Winnipeg, MB, Canada R3T 5V6 [email protected] G. Gary Wang School of Engineering Science Simon Fraser University Surrey, BC, Canada V3T 0A3 [email protected] Abstract Computational tools such as finite element analysis and simulation are widely used i...

This paper presents the performance comparison of two architectures of neural networks: multi-layer perceptron (MLP) neural networks and radial basis function (RBF) neural networks on face recognition system (FRS). We are training MLP using different variants of back-propagation (BP) algorithm. AT&T database has been used for performance comparison. The BP is gradient descent based iterative al...