在分析液压 AGC的组成元件及其动态特性的基础上 ,利用神经网络具有逼近任何非线性函数且具有自学习和自适应的能力 ,建立基于时间序列的前馈动态模型辨识结构 ,应用扩展 BP算法对轧机液压 AGC力控制系统进行非线性预测 ,将预测结果应用最小二乘辨识方法进行线性系统的特征参数辨识 ,仿真及实测结果表明此方法行之有效 ,为轧机液压 AGC的辨识提供了新途径。 Based on the analysis of components and their dynamic characteristics of hydraulic AGC, neural network has the ability of approximating any non-linear function and has the ability of self-learning and self-adaptation. The structure of feedforward dynamic model identification based on time series is established. By using extended BP algorithm The nonlinear AGC force control system of rolling mill is predicted by nonlinear method. The least squares identification method is used to identify the characteristic parameters of linear system. The simulation and measured results show that this method is effective, which provides a new way for the identification of mill hydraulic AGC .