Title: Performance of auto-configuring RBF networks trained with significant patterns

Authors: T.N. Nagabhushan, S.K. Padma, Bhanu Prasad

Addresses: Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysore 570 006, India. ' Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysore 570 006, India. ' Department of Computer and Information Sciences, Florida A&M University, Tallahassee, FL 32307, USA

Abstract: This paper presents two new ideas to improve the performance of Radial Basis Function (RBF) networks. In the first instance, we choose a set of patterns for training, which are closer to decision boundaries, from different classes of training samples that constitute the input space. We call those set of patterns significant patterns and discuss their selection process from the given data set. Secondly, we use these significant patterns to train Adaptive incremental learning RBF network and Resource Allocating Network (RAN). The learning curves and generalisation characteristics of the generated RBF networks are presented. The performance results are discussed.

Keywords: RBF neural networks; incremental learning; medoids; significant patterns; generalisation; radial basis function; resource allocating networks; training.

DOI: 10.1504/IJSISE.2009.029328

International Journal of Signal and Imaging Systems Engineering, 2009 Vol.2 No.1/2, pp.41 - 50

Received: 06 Dec 2008
Accepted: 27 Jan 2009
Published online: 19 Nov 2009 *

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