Remarks on Adaptive-Type Hypercomplex-Valued Neural Network-Based Feedforward Feedback Controller
Remarks on Adaptive-Type Hypercomplex-Valued Neural Network-Based Feedforward Feedback Controller
复制标题
基于自适应型超复值神经网络的前馈反馈控制器评述
DOI：
10.1109/cit.2017.16
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Kazuhiko Takahashi
中科院分区：
文献类型：
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作者：
Kazuhiko Takahashi
在这项研究中，我们研究了自适应型前馈反馈控制器的控制性能，采用多层超复值神经网络。该控制系统由神经网络和反馈控制器组成，利用多层超复值神经网络和反馈控制器的和来在线合成被控对象的控制输入，以跟踪被控对象输出到参考模型产生的期望输出。通过控制多输入多输出离散时间非线性对象的计算实验，验证了基于超复值神经网络的前馈反馈控制器的性能。实验结果表明了该控制器的可行性和有效性。
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.
DOI：
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发表时间：
2004
期刊：
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影响因子：
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作者：
Hiromi Kusamichi;T. Isokawa;N. Matsui;Y. Ogawa
通讯作者：
Hiromi Kusamichi;T. Isokawa;N. Matsui;Y. Ogawa