【摘要】 论述了一种基于Ｈｏｐｆｉｅｌｄ神经网络线性系统参数辨识方法，导出了辨识的充分条件，并将该方法应用于鼠笼式电机传动系统的转动惯量，风阻系数以及负载转矩的辨识。仿真结果表明，即使上述参量的不正确值设置而导致系统运行状况恶化时，该方法仍能保证辨识结果收敛于正确值。更多还原

【Abstract】 The paper introduces a method of linear system parameter identification based on Hopfield Neural Network. This method assumed that the input of Neural Network was system state variables which were tested and delayed by sensor. Full condition of identification was deduced, and this method was applied to identification of revolve value J , resistance R Ω and load torque T L of squirrel cage motor driving system. Simulation results showed that this method can make the identification results converqe in true value even if the system condition is deteriorated by not correct parameters of the controller.更多还原