Improvement of learning algorithms for RBF neural networks in a helicopter sound identification system

This paper presents a set of optimizations in learning algorithms commonly used for training radial basis function neural networks. These optimizations are applied to a RBF neural network used in identifying helicopter types processing their rotor sounds. The first method uses an optimum learning rate in each iteration of train process. This method increases the speed of learning process and also achieves an absolute stability in network response. Another modification is applied to Quick Propagation (QP) method as a generalization which attains more learning speed. Finally, we introduced the General Optimum Steepest Descent (GOSD) method, which contains both improvements in learning RBF networks. All modified methods are employed in training a system which recognizes helicopters’ rotor sounds exploiting a RBF neural network. Comparing results of these learning methods with the previous ones yields interesting outcomes.