Signal Classification Through Multifractal Analysis and Complex Domain Neural Networks  

Oleh:

Kinsner, Witold ; Cheung, Vencent ; Cannons, Kevin ; Pear, Joseph ; Martin, Toby

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Systems, Man, and Cybernetics: Part C Applications and Reviews vol. 36 no. 2 (Mar. 2006), page 196-203.
Topik: Classification; Complex Domain Neural Network (CNN); Multifractal Analysis; Probablistic Neural Network (PNN)

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II69.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

This paper describes a system capable of classifying stochastic self-affine nonstationary signals produced by nonlinear systems. The classification and the analysis of these signals are important because these are generated by many real-world processes. The first stage of the signal classification process entails the transformation of the signal into the multifractal dimension domain, through the computation of the variance fractal dimension trajectory (VFDT). Features can then be extracted from the VFDT using a Kohonen self-organizing feature map. The second stage involves the use of a complex domain neural network and a probabilistic neural network to determine the class of a signal based on these extracted features. The results of this paper show that these techniques can be successful in creating a classification system which can obtain correct classification rates of about 87% when performing classification of such signals without knowing the number of classes.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali