A new method of reducing network complexity in probabilistic neural network for target identification

Abstract

The performance of Probabilistic Neural Network (PNN) in object identification has been investigated with reduced complexity retaining its remarkable recognition accuracy. A new method has been proposed to reduce the network complexity in PNN by performing the harmonics mean, geometry mean, arithmetic mean and root mean square values on the training samples. The simulation results have proven a significant improvement in network complexity reduction in PNN while maintaining the recognition accuracy. This would greatly enhance the applicability of PNNs.