Improvement of learning rate for RBF neural networks in a helicopter sound identification system introducing two-phase OSD learning method

Abstract

This paper presents a novel approach in learning algorithms commonly used for training radial basis function neural networks. This approach could be used in applications which need real-time capabilities for retraining RBF neural networks. Proposed method is a two-phase learning algorithm which optimizes the functionality of optimum steepest decent (OSD) learning method. This methodology speeds to attain better performance by initial calculation of centre and width of RBF units. This method has been tested in an audio processing application, a system for identifying helicopters using their sound of rotors. Comparing results obtained by employing different learning strategies shows interesting outcomes as have come in this paper.