Abstract
The paper discusses a Hopfield-Neural-Network (HNN) based linear system parameters' identification scheme in part I, under the assumption that HNN inputs are the detected system states delayed by sensors. Sufficient condition of correct identification is derived. The scheme is used for identification of rotation inertia (J), speed damping coefficient (Rω) and load torque (TL) of induction motor drive system. Simulation results show that even under bad working condition caused by incorrect setting to the controllers, the derived scheme guarantees correct identify results.
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