Identification of Nonlinear Dynamic Processes with Unknown and Variable Dead Time Using an Internal Recurrent Neural Network
Identification of Nonlinear Dynamic Processes with Unknown and Variable Dead Time Using an Internal Recurrent Neural Network
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使用内部循环神经网络识别具有未知且可变死区时间的非线性动态过程
DOI：
10.1021/ie00044a025
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发表时间：
1995
期刊：
影响因子：
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通讯作者：
D. Himmelblau
中科院分区：
文献类型：
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作者：
Yi Cheng;T. Karjala;D. Himmelblau
提出了一种通过内部递归网络(IRN)模型辨识具有未知和可能可变死区的非线性动态过程的方法。结果表明，具有足够隐节点的IRN可以直接用于具有固定或可变死区的非线性动态过程的辨识。如果使用进程输入窗口而不仅仅是当前进程输入作为IRN模型的输入，则可以减少IRN模型中隐藏节点的数量，并且IRN的预测性能对于具有大且可变的死区时间的进程来说是提高的。对具有输送滞后的pH中和过程的仿真结果表明了所提方法的有效性
Methods for identifying a nonlinear dynamic process with unknown and possibly variable dead times via an internal recurrent network (IRN) model are proposed. It is shown that an IRN with sufficient hidden nodes can be used directly for the identification of a nonlinear dynamic process with fixed or variable dead times. If a process input window rather than just the current process input is used as the input to an IRN model, the number of hidden nodes in the IRN model can be reduced, and the prediction performance of the IRN improves for processes with large, and variable, dead times. Simulation results for a pH neutralization process with transportation lags demonstrate the effectiveness of the proposed methods