采用简化迟滞算子对模型进行预处理后，构造神经网络实现模型的辨识。
Then a neural network was built to identify the new model based on simplified hysteresis operators.
提出了两个动态神经网络串联的混合神经网络动态迟滞模型，用以逼近压电陶瓷的迟滞特性。
The hybrid neural network dynamic hysteresis model, which consists of two dynamic neural networks in a cascade form, is proposed to approximate the hysteresis characteristics of piezoceramic actuator.
将该网络应用于纳米定位系统压电陶瓷执行器迟滞建模中，可以降低建模误差，实验结果验证了该方法的有效性。
The network can reduce the modeling error for the piezoelectric actuator of a nanometer positioning system. Experimental results proved validity of the algorithm.
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