Classification of chaotic circuit output patterns with probabilistic neural networks

This study focused on the classification of chaotic circuit behaviors with probabilistic neural network (PNN). Although, chaotic circuit outputs track similar traces for the defined parameters, still the circuit outputs preserve their own random characteristics at each trial. PNN is an effective tool for classification of pattern recognition problems. Inherited features of PNN are very compatible with the chaotic circuit output classification problem and it provides satisfying performance. The selection of the proper features in the feature extraction step defines the performance of the classification significantly. In order to, compare classification performance of the PNN, different feature vectors are employed in the training process. Moreover, the spread parameter is a considerably vital factor for the performance of the network. The simulation results and the corresponding illustrations for the performance analysis are also given.