Hopfield-based adaptive state estimators

Hopfield networks have been applied to the problem of system identification. Luenberger observers have long been used for estimation of unmeasurable states of linear systems. The mathematical derivation of an adaptive observer based on integration of the two techniques is presented. The identification of unknown multiple input multiple output (MIMO) systems with noise corrupted measurements is described. Simulation results for different plant conditions are detailed.<
>