System identification based on a generalized ADALINE neural network

System identification of linear time-varying systems consists of estimation of system parameters which change with time. In this paper, we present an online identification method for such systems based on a generalized ADAptive LINear Element (ADALINE) neural network. It is well known ADALINE is slow in convergence which is not appropriate for online application and identification of time varying system. Two techniques are proposed to speed up convergence of learning, thus increase the capability of tracking time varying system parameters. One idea is to introduce a momentum term to the weight adjustment during convergence period. The other technique is to train the generalized ADALINE network multiple epochs with data from a sliding window of the system's input output data. Simulation results show that the proposed method 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.