Remarks on Adaptive-type Hypercomplex-valued Neural Network-based Feedforward Feedback Controller

被引：13
|
作者：
Takahashi, Kazuhiko [1 ]
机构：
[1] Doshisha Univ, Fac Sci & Engn, Informat Syst Design, Kyoto 6100321, Japan
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY (CIT) | 2017年
关键词：
feedforward feedback controller; complex neural network; bicomplex neural network; quaternion neural network;
D O I：
10.1109/CIT.2017.16
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：151 / 156
页数：6
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