Classification of chaotic circuit output patterns with probabilistic neural networks
Küçük Resim Yok
Tarih
2011
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Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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. © 2011 IEEE.
Açıklama
2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011 -- 20 April 2011 through 22 April 2011 -- Antalya -- 85528
Anahtar Kelimeler
Chaotic Circuits, Classification Performance, Effective Tool, Feature Vectors, Pattern Recognition Problems, Performance Analysis, Probabilistic Neural Networks, Simulation Result, Training Process, Electric Network Analysis, Feature Extraction, Networks (Circuits), Signal Processing, Neural Networks
Kaynak
2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011
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