A technique for on-line parameter estimation based on an analog artificial neural net structure

Abstract

In this paper, we present a technique for on-line parameter estimation based on an analog artificial neural net structure. The architecture adopted is the so-called Hopfield net and the continuous state, or analog, version is used. The fundamental algorithm constructed is shown to be globally stable in the sense of Liapunov. This is further extended and developed to incorporate on-line recursion, and for systems with parameters that change over time, the most general version of our neural net algorithm incorporates recursion using data in exponentially weighted windows. Simulation results are provided to show the performance of the on-line estimator and its ability to track changing parameters with the use of exponential weighting. Additional simulation results are also included which shows the robustness of the estimator in the presence of unmodelled dynamics.