提出了辨识双线性Ｈａｍｍｅｒｓｔｅｉｎ模型的稳态与动态辨识相结合的集成辨识方法．该方法利用稳态信息获取稳态模型的强一致性估计，并利用稳态模型通过神经网络逼近获得了静态非线性增益的估计，再利用动态信息辨识获取双线性Ｈａｍｍｅｒｓｔｅｉｎ模型的双线性系统未知参数的一致性估计．仿真结果表明了该方法的有效性和实用性． An integrated identification method based on the combination of steady state and dynamic identification of bilinear Hammerstein model is proposed. The method uses the steady-state information to obtain the strong consistency estimation of the steady-state model and obtains the static nonlinear gain estimation by using the steady-state model approximated by the neural network. The dynamic information is used to obtain the bilinear system of the bilinear Hammerstein model Consistency estimation of unknown parameters. Simulation results show the effectiveness and practicability of this method.