A Generalized ADALINE Neural Network for System Identification

Abstract

In this paper, we present a generalized adaptive linear element (ADALINE) neural network and its application to system identification of linear time-varying systems. It is well known ADALINE is slow in convergence which is not appropriate for online application and identification of time varying system. The proposed generalized ADALINE, called GADALINE, has two aspects of generalization: i) the input now consists of a tapped delay line of the system input signal and a tapped delay line of the system output feedback; and ii) the adaptive learning is further generalized by adding a momentum term to the weight adjustment during convergence period. The GADALINE's learning curve is smoothed by turning off the momentum once the error is within a given small number. Simulation results show that GADALINE provides a much faster convergence speed and better tracking of time varying parameters. The low computational complexity makes this method suitable for online system identification and real time adaptive control applications.