Neural networks for frequency line tracking

Author

Adams, Gregory J. ; Evans, Robin J.

Author_Institution

Dept. of Electr. & Comput. Eng., Newcastle Univ., NSW, Australia

Volume

42

Issue

4

fYear

1994

fDate

4/1/1994 12:00:00 AM

Firstpage

936

Lastpage

941

Abstract

This paper investigates the application of neural networks to frequency line tracking. Recently, hidden Markov models (HMM´s) have been successfully applied to this problem, and here, we study a neural network architecture called Mnet, which is based on an underlying Markov model representation. A supervised learning algorithm is developed for Mnet, and a method of analytically deriving the connection weights for the Mnet is also mentioned. Two more conventional neural networks are also studied; a multilayer feedforward network and a multilayer network with feedback. The simulation results show that all three neural networks are comparable in performance to a hidden Markov model when applied to the frequency line tracking problem

Keywords

feedforward neural nets; hidden Markov models; learning (artificial intelligence); recurrent neural nets; signal processing; tracking; HMM; Mnet architecture; connection weights; frequency line tracking; hidden Markov models; multilayer feedback network; multilayer feedforward network; neural networks; signal processing; supervised learning algorithm; Artificial neural networks; Australia; Frequency; Hidden Markov models; Multi-layer neural network; Neural networks; Neurofeedback; Signal processing; Signal processing algorithms; Supervised learning;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.285656

Filename

285656