Çekli, S.Uzuno?lu, C.P.2024-07-122024-07-1220119.78146E+1210.1109/SIU.2011.59296152-s2.0-79960407347https://doi.org/10.1109/SIU.2011.5929615https://hdl.handle.net/20.500.12415/74082011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011 -- 20 April 2011 through 22 April 2011 -- Antalya -- 85528This 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. © 2011 IEEE.trinfo:eu-repo/semantics/closedAccessChaotic CircuitsClassification PerformanceEffective ToolFeature VectorsPattern Recognition ProblemsPerformance AnalysisProbabilistic Neural NetworksSimulation ResultTraining ProcessElectric Network AnalysisFeature ExtractionNetworks (Circuits)Signal ProcessingNeural NetworksConference Object173N/A170