AR parameter estimation by a feedback neural network

As known, estimation of autoregressive (AR) parameters is fundamental in signal processing and time-series analysis. It can be widely used in many applications. Several sequential algorithms have been developed, such as the well-known Levinson-Durbin algorithm, Burg algorithm and Marple algorithm. Much research is still ongoing in this field. In this paper, we presesnt a new approach that takes advantage of a feedback recurrent neural network to estimate the AR parameters in a parallel way. It is a kind of dynamic mechanism that implement calculation by dynamic evolution. The algorithm was provided and expressed by applying the neural network technique. It also gives the strict proof of stability of the dynamic system and condition of convergence. Besides, we contribute the practical and simple hardware. The algorithm can be easily and simply converted into hardware so that it runs very quickly. The dynamic system is expressed by the first-order ordinary differential system, so we use a numerical method to approximate the solution. The simulation has been done by Runge-Kutta method with respect to simulated signal and auditory brainstem response (ABR) which is commonly used in medical research. Results showed that the method is effective to estimate AR parameters. (C) 1997 Published by Elsevier Science B.V.