非线性系统内模控制的逆递归神经网络

Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207) Pub Date : 1998-06-21 DOI:10.1109/ACC.1998.703554

C. Kambhampati, R. Craddock, M. Tham, K. Warwick

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引用次数: 4

摘要

在本文中，我们展示了如何将一组最近导出的关于循环神经网络的理论结果应用于非线性装置的内模控制系统的生产。结果包括确定递归神经网络的相对顺序和这种网络的可逆性。在不需要重新训练神经网络对象模型的情况下，产生闭环控制器。验证了闭环控制器的稳定性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Inverting recurrent neural networks for internal model control of nonlinear systems

In this paper, we show how a set of recently derived theoretical results for recurrent neural networks can be applied to the production of an internal model control system for a nonlinear plant. The results include determination of the relative order of a recurrent neural network and invertibility of such a network. A closed loop controller is produced without the need to retrain the neural network plant model. Stability of the closed-loop controller is also demonstrated.

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Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207)

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