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Paper Abstract and Keywords
Presentation 2020-12-14 10:45
A Study on Relaxation of Network Restriction in Deep-Unfolding aided Consensus
Shoya Ogawa, Ishii Koji (Kagawa Univ.) WBS2020-11 ITS2020-7 RCC2020-14
Abstract (in Japanese) (See Japanese page) 
(in English) In the consensus problem with a complex network, the convergence performance deeply depends on the given parameters, i.e., the weighting values given at individual edges. Recently, Kishida et.al., have proposed to apply a deep learning technique into the consensus problem and shown that the deep-leaning aided consensus problem can significantly enhance the convergence performance. The deep-learning aided consensus problem tries to learn the samples of the consensus problem with the fixed network topology but different initial values. However, since this system is designed only for the given network topology, Kishida’s deep-learning aided consensus cannot apply to the system with other network topology. To relax the restriction about applying network topology, this study proposes a statistical approach to give the wighting values at individual edges. We first gather the weight values calculated by Kishida’s deep-learning aided consensus with different network topologies and sort the gathered data into the cases corresponding to the time and the number of neighbor agents. From the computer simulation, the shape of pdf of sorted data can be seen as a Gaussian distribution and thus we set up a hypothesis that optimal weighting value follows a Gaussian distribution. Then, this study proposes to assign the random variable which follows the Gaussian distribution into the edge with the same time-index and number of neighbors. Computer simulations show that the proposed system cannot achieve better performance than the conventional system with fixed weighting value. Thus, this work tries to investigate the causes why the proposed deep-learning aided consensus cannot efficiently work.
Keyword (in Japanese) (See Japanese page) 
(in English) consensus problem / data-driven algorithm / deep-unfolding / gaussian approximation / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 292, RCC2020-14, pp. 19-24, Dec. 2020.
Paper # RCC2020-14 
Date of Issue 2020-12-07 (WBS, ITS, RCC) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF WBS2020-11 ITS2020-7 RCC2020-14

Conference Information
Committee ITS WBS RCC  
Conference Date 2020-12-14 - 2020-12-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) ITS Communications, Reliable Communication and Control, Radar and Sensing, etc. 
Paper Information
Registration To RCC 
Conference Code 2020-12-ITS-WBS-RCC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Relaxation of Network Restriction in Deep-Unfolding aided Consensus 
Sub Title (in English)  
Keyword(1) consensus problem  
Keyword(2) data-driven algorithm  
Keyword(3) deep-unfolding  
Keyword(4) gaussian approximation  
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1st Author's Name Shoya Ogawa  
1st Author's Affiliation Kagawa University (Kagawa Univ.)
2nd Author's Name Ishii Koji  
2nd Author's Affiliation Kagawa University (Kagawa Univ.)
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Speaker Author-1 
Date Time 2020-12-14 10:45:00 
Presentation Time 25 minutes 
Registration for RCC 
Paper # WBS2020-11, ITS2020-7, RCC2020-14 
Volume (vol) vol.120 
Number (no) no.290(WBS), no.291(ITS), no.292(RCC) 
Page pp.19-24 
#Pages 6 
Date of Issue 2020-12-07 (WBS, ITS, RCC) 

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