多输入线性对象的神经网络综合实例

A. Voevoda, D. Romannikov
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引用次数: 0

摘要

本文提出了一种基于强化学习方法的神经控制器综合方法。所提出的方法应适用于具有多个控制输入和单个输出的多通道对象。从控制器综合的角度来看,这些对象是复杂的对象,应用已知的经典方法特别是多项式综合方法可能无法得到结果。另一方面,控制器与神经网络的综合允许调节这类对象。文中给出了将该方法应用于具有两个输入和一个输出的对象的实例。
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An Example of Synthesis on the Neural Networks for the Linear Objects with Multiple Inputs
The method of synthesis of the neural controllers using, that is based on the reinforcement learning approaches, is proposed in the paper. The proposed method should be applied to multichannel objects with several control inputs and a single output. Such objects are complex objects from the controller synthesis point of view and the application of the known classical methods particularly of the polynomial synthesis method might not give results. On the other hand, the synthesis of controllers with neural networks allows to regulate such types of objects. An example of the application of the proposed method to the object with two inputs and one output is shown in the paper.
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