自适应型超复值神经网络前馈反馈控制器评述

2017 IEEE International Conference on Computer and Information Technology (CIT) Pub Date : 2017-08-01 DOI:10.1109/CIT.2017.16

Kazuhiko Takahashi

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引用次数: 11

摘要

本文研究了一种基于多层超复值神经网络的自适应前馈控制器的控制性能。控制系统由神经网络和反馈控制器组成，利用多层超复杂值神经网络和反馈控制器的总和在线合成被控对象的控制输入，跟踪被控对象的输出到参考模型生成的期望输出。通过控制多输入多输出离散非线性对象的计算实验，评估了基于超复值神经网络的前馈反馈控制器的性能和特性。实验结果表明了该控制器的可行性和有效性。

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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Remarks on Adaptive-Type Hypercomplex-Valued Neural Network-Based Feedforward Feedback Controller

In this study, we investigate the control performance of an adaptive-type feedforward feedback controller using multilayer hypercomplex-valued neural network. The control system consists of a neural network and a feedback controller, whereby the control input of a plant is synthesised online by using the sum of the multilayer hypercomplex-valued neural network and the feedback controller to track the plant output to the desired output generated by a reference model. Computational experiments to control a multiple-input and multiple-output discrete-time nonlinear plant are conducted to evaluate the capability and characteristics of the hypercomplex-valued neural network-based feedforward feedback controller. Experimental results show the feasibility and effectiveness of the proposed controller.

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2017 IEEE International Conference on Computer and Information Technology (CIT)

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