bibtype	J - Journal Article
ARLID	0457337
utime	20240103212008.7
mtime	20160316235959.9
SCOPUS	85014589668
WOS	000396232000002
DOI	10.1109/TSMC.2015.2502427
title (primary) (eng)	Scalable Harmonization of Complex Networks With Local Adaptive Controllers
specification	page_count 11 s. media_type P
serial	ARLID cav_un_epca*0430806 ISSN 2168-2216 title IEEE Transactions on Systems Man Cybernetics-Systems volume_id 47 volume 3 (2017) page_num 394-404 publisher name Institute of Electrical and Electronics Engineers
keyword	Adaptive control
keyword	Adaptive estimation
keyword	Bayes methods
keyword	Complex networks
keyword	Decentralized control
keyword	Feedback
keyword	Feedforward systems
keyword	Recursive estimation
author (primary)	ARLID cav_un_auth*0101124 full_dept (cz) Adaptivní systémy full_dept (eng) Department of Adaptive Systems department (cz) AS department (eng) AS full_dept Department of Adaptive Systems share 70 name1 Kárný name2 Miroslav institution UTIA-B fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author	ARLID cav_un_auth*0329444 share 30 name1 Herzallah name2 R. country GB
source	url http://library.utia.cas.cz/separaty/2016/AS/karny-0457337.pdf
cas_special	project ARLID cav_un_auth*0292725 project_id GA13-13502S agency GA ČR abstract (eng) Computational and communication complexities call for distributed, robust, and adaptive control. This paper proposes a promising way of bottom-up design of distributed control in which simple controllers are responsible for individual nodes. The overall behavior of the network can be achieved by interconnecting such controlled loops in cascade control for example and by enabling the individual nodes to share information about data with their neighbors without aiming at unattainable global solution. The problem is addressed by employing a fully probabilistic design, which can cope with inherent uncertainties, that can be implemented adaptively and which provide a systematic rich way to information sharing. This paper elaborates the overall solution, applies it to linear-Gaussian case, and provides simulation results. RIV BB FORD0 10000 FORD1 10100 FORD2 10103 reportyear 2018 num_of_auth 2 mrcbC52 4 A hod 4ah 20231122141555.1 inst_support RVO:67985556 permalink http://hdl.handle.net/11104/0258400 mrcbC64 1 Department of Adaptive Systems UTIA-B 10200 COMPUTER SCIENCE, CYBERNETICS confidential S mrcbC86 2 Article Automation Control Systems|Computer Science Cybernetics mrcbC86 2 Article Automation Control Systems|Computer Science Cybernetics mrcbC86 2 Article Automation Control Systems|Computer Science Cybernetics mrcbT16-e AUTOMATIONCONTROLSYSTEMS|COMPUTERSCIENCECYBERNETICS mrcbT16-j 1.073 mrcbT16-s 1.303 mrcbT16-B 62.96 mrcbT16-D Q2 mrcbT16-E Q1 arlyear 2017 mrcbTft \nSoubory v repozitáři: karny-0457337.pdf mrcbU14 85014589668 SCOPUS mrcbU34 000396232000002 WOS mrcbU63 cav_un_epca*0430806 IEEE Transactions on Systems Man Cybernetics-Systems 2168-2216 2168-2232 Roč. 47 č. 3 2017 394 404 Institute of Electrical and Electronics Engineers