Title :
On the use of neural networks in the generalized likelihood ratio test for detecting abrupt changes in signals
Author :
Fancourt, Craig L. ; Principe, Jose C.
Author_Institution :
Dept. of Electr. Eng., Florida Univ., Gainesville, FL, USA
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
Keywords :
learning (artificial intelligence); maximum likelihood estimation; neural nets; prediction theory; signal detection; time series; generalized likelihood ratio test; hybrid algorithm; learning; maximum likelihood estimation; neural networks; signal change detection; time series; Computational efficiency; Computational modeling; Computer networks; Intelligent networks; Laboratories; Neural engineering; Neural networks; Random processes; Signal processing; Testing;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.857904
