为典型工业过程的AIMNC战略提供适当的训练集

Jasmin Igic, M. Bozic
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引用次数: 1

摘要

在这里,我们讨论了应用于典型工业过程的基于近似内模型的神经控制(AIMNC)应该如何选择训练数据集。在所考虑的控制策略中,只需要离线训练一个神经网络(NN),即多层神经网络(MLNN),它是被控对象的神经模型。由神经模型直接得到逆神经控制器,无需进一步训练。仿真结果表明,对于得到的训练集充足的神经网络模型,AIMNC策略具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An adequate training set for the AIMNC strategy for typical industrial processes
Here we have discussed how the training data set should be selected for the Approximate Internal Model-based Neural Control (AIMNC) applied to the typical industrial processes. In the considered control strategy only one neural network (NN), Multi Layer NN (MLNN), which is the neural model of the plant, should be trained off-line. An inverse neural controller can be directly obtained from the neural model without necessity of a further training. Simulations demonstrate performance of the AIMNC strategy for NN model obtained with adequate training set.
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