Neural networks for frequency line tracking

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