分析了动态递归神经网络系统辨识的参数学习算法。
The parameter learning algorithm of dynamic recurrent neural network based on system identification is analyzed. D.
它通过构造伪输出辨识被控对象参数，引进反馈误差，实现对解耦神经网络的在线训练。
It can identify the parameters of a controlled object by forming a fake output and bring in a feedback error for performing an on-line training to decouple the neural network.
对处于扰动状态下的预分馏塔的仿真结果表明，该算法可以有效地解决一类多神经网络模型的在线参数辨识问题。
Simulation results of a disturbed pre-fractionator show that this algorithm can be used to solve on-line parameters-recognized problem of a kind of multi- neural networks model effectively.
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