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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 29, Number 6(1) (2017)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 699-711
S&M1361 Research Paper of Special Issue https://doi.org/10.18494/SAM.2017.1468 Published: June 7, 2017 Design of Quaternion-Neural-Network-Based Self-Tuning Control Systems [PDF] Kazuhiko Takahashi, Yusuke Hasegawa, and Masafumi Hashimoto (Received September 12, 2016; Accepted January 5, 2017) Keywords: quaternion neural network, self-tuning controller, PID controller, nonlinear plant, reference model
In this study, we investigate the control performance of an adaptive controller using a multilayer quaternion neural network. The control system is a self-tuning controller, the control parameters of which are tuned online by the quaternion neural network to track plant output to follow the desired output generated by a reference model. A proportional–integral–derivative (PID) controller is used as a conventional controller, the parameters of which are tuned by the quaternion neural network. Computational experiments to control a single-input single-output (SISO) discrete-time nonlinear plant are conducted to evaluate the capability and characteristics of the quaternion-neural-networkbased self-tuning PID controller. Experimental results show the feasibility and effectiveness of the proposed controller.
Corresponding author: Kazuhiko Takahashi Cite this article Kazuhiko Takahashi, Yusuke Hasegawa, and Masafumi Hashimoto, Design of Quaternion-Neural-Network-Based Self-Tuning Control Systems, Sens. Mater., Vol. 29, No. 6, 2017, p. 699-711. |
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