Signal Classification through Multifractal Analysis and Complex Domain Neural Networks

Authors:
W. Kinsner;V. Cheung;K. Cannons;J. Pear;T. Martin
Affiliations:
-;-;-;-;-
Venue:
ICCI '03 Proceedings of the 2nd IEEE International Conference on Cognitive Informatics
Year:
2003

Citing 2
Cited 2

Self-organization and associative memory: 3rd edition

Self-organization and associative memory: 3rd edition
Signal and image processing with neural networks: a C++ sourcebook

Signal and image processing with neural networks: a C++ sourcebook

EEG-Based Classification of Motor Imagery Tasks Using Fractal Dimension and Neural Network for Brain-Computer Interface

IEICE - Transactions on Information and Systems
Singularity spectrum analysis of windowed seismograms

Proceedings of the 8th International Conference on Frontiers of Information Technology

Quantified Score

Hi-index	0.00

Visualization

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.