On the use of neural networks in the generalized likelihood ratio test for detecting abrupt changes in signals

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 test for the purpose of detecting abrupt nonstationary 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.