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递推多步预测    
相关语句
  recurrence multi-step prediction
    In addition, by comparing results of simulation and recurrence multi-step prediction, the conclusion of direct multi-step predicted control is more accurate than recurrence multi-step prediction is obtained.
    并将仿真结果与利用递推多步预测方法的结果进行了比较 ,得到直接多步预测控制较递推多步预测更准确的结论。
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In order to control a nonlinear system, a model of recurrent multi-step prediction of the future process outputs which is constructed based on multiplayer neural network is developed and combined with a self-tuning PID controller. In the control process, an extended Kalman filter algorithm is introduced to quicken neural training. Simulation studies show the effectiveness and good performance.

提出在利用前馈神经网络对非线性系统建模的基础上,对系统输出实现递推多步预测,并且结合自整定PID方法,实现非线性系统控制,神经网络在线辨识时采用学习速度较快的扩展Kalman滤波方法,仿真实验表明了该方法的有效性.

A feed forward network is used to model a nonlinear system by using the multi-step predicive method And the control of the nonlinear system is achieved by usign another neural network as the controller.

针对一些复杂的非线性系统用基于线性模型的预测控制器控制效果不理想的问题 ,本文提出在利用前馈网络对非线性系统建模的基础上 ,对系统输出实现递推多步预测 ,并且结合非线性PID方法 ,用另一前馈神经网络作为控制器 ,实现对非线性系统的控制。神经网络的在线辨识采用梯度法。仿真实验验证了方法的有效性。

To against the problem of unsatisfied control effects are obtained in neural network reverse dynamic control method in complicated non-linear systems, the author states the measure that multiple-step prediction is taken directly for the controlled object, and reverse control based on neural network is implemented by using multi-step performance function to the system. In addition, by comparing results of simulation and recurrence multi-step prediction, the conclusion of direct multi-step predicted control is...

To against the problem of unsatisfied control effects are obtained in neural network reverse dynamic control method in complicated non-linear systems, the author states the measure that multiple-step prediction is taken directly for the controlled object, and reverse control based on neural network is implemented by using multi-step performance function to the system. In addition, by comparing results of simulation and recurrence multi-step prediction, the conclusion of direct multi-step predicted control is more accurate than recurrence multi-step prediction is obtained.

针对一些复杂的非线性系统在神经网络逆动态控制方法下控制效果不理想的问题 ,本文提出对被控对象进行直接多步预测 ,利用多步预测性能指标函数对系统实现基于神经网络的逆控制。并将仿真结果与利用递推多步预测方法的结果进行了比较 ,得到直接多步预测控制较递推多步预测更准确的结论。

 
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