NDLI: Signal classification through multifractal analysis and complex domain neural networks

Content Provider	IEEE Xplore Digital Library
Author	Kinsner, W. Cheung, V. Cannons, K. Pear, J. Martin, T.
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada (Kinsner, W.; Cheung, V.; Cannons, K.)
Abstract	This paper describes a system capable of classifying stochastic, self-affine, nonstationary signals produced by nonlinear systems. The classification and analysis of these signals is important because they 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.
Starting Page	41
Ending Page	46
File Size	382970
Page Count	6
File Format	PDF
ISBN	0769519865
DOI	10.1109/COGINF.2003.1225951
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-08-20
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern classification Fractals Signal analysis Neural networks Signal processing Marine animals Stochastic systems Nonlinear systems Sampling methods Data compression
Content Type	Text
Resource Type	Article

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