Optimized time-frequency distributions for signal classification with feed-forward neural networks

Markus Till; Stephan Rudolph

doi:10.1117/12.380583

30 March 2000 Optimized time-frequency distributions for signal classification with feed-forward neural networks

Markus Till, Stephan Rudolph

Proceedings Volume 4055, Applications and Science of Computational Intelligence III; (2000) https://doi.org/10.1117/12.380583
Event: AeroSense 2000, 2000, Orlando, FL, United States

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.

Citation Download Citation

Markus Till and Stephan Rudolph "Optimized time-frequency distributions for signal classification with feed-forward neural networks", Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); https://doi.org/10.1117/12.380583

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