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Proceedings Paper

Optimized time-frequency distributions for signal classification with feed-forward neural networks
Author(s): Markus Till; Stephan Rudolph
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Paper Abstract

Successful signal classification requires the selection of a problem-oriented signal representation and the extraction of a feature vector for final classification. The choice of the optimum representation and optimum features needs some a prior knowledge of the inherent structure and symmetries of the time series as well as the differences between the classes to be distinguished. One central question is: which time series are really similar. An important task for successful signal classification is therefor a clear concept of similarity. A very powerful tool for signal analysis are time-frequency distributions. They are 2D functions that indicate the time-varying frequency content of 1D signals. Each bilinear time-frequency distribution corresponds to a kernel function that controls the properties of the integral transform. However, there is no single time-frequency distribution which is optimal for all problems. It is still an unsolved problem to determine the optimum time-frequency distribution for a given signal and analysis task. In this work a data-driven adaptive time-frequency distribution is presented. The kernel of the time-frequency distribution is parameterized and adapts to the maximal classification rate. The subsequent feature extraction results from calculating the joint moments of the time-frequency distribution. Dimensional analysis is used for defining a similarity concept for time series analysis through generation of a set of dimensionless classification numbers.

Paper Details

Date Published: 30 March 2000
PDF: 12 pages
Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); doi: 10.1117/12.380583
Show Author Affiliations
Markus Till, Univ. Stuttgart (Germany)
Stephan Rudolph, Univ. Stuttgart (Germany)


Published in SPIE Proceedings Vol. 4055:
Applications and Science of Computational Intelligence III
Kevin L. Priddy; Paul E. Keller; David B. Fogel, Editor(s)

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