On the Use of Neural Networks in the Generalized Likelihood Ratio Test for Detecting Abrupt Changes in Signals

Authors:
Craig L. Fancourt;Jose C. Principe
Affiliations:
-;-
Venue:
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2 - Volume 2
Year:
2000

Citing 0
Cited 2

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Neural Networks

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Abstract

With the advent of efficient algorithms and fast computers for training neural networks, it is now feasible to employ neural network predictors in the generalized likelihood ratio (GLR) test for detecting abrupt non-stationary changes in the dynamics of a time series. We examine some of the special issues involved and present some simulation results validating the new hybrid algorithm.