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008			171110s2018 gw | s |||| 0|eng d
020			_a9783319680224
024	7		_a10.1007/978-3-319-68022-4 _2doi
040			_bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.76.I58 _bF346 2018 EB
100	1		_aFagnani, Fabio. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/nb2015015126 _1http://viaf.org/viaf/286858772/
245	1	0	_aIntroduction to Averaging Dynamics over Networks _cby Fabio Fagnani, Paolo Frasca.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XII, 135 páginas 22 ilustraciones, 3 ilustraciones a color.)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aLecture Notes in Control and Information Sciences, _x0170-8643 _v472
490	0		_aEngineering (Springer-11647)
505	0		_aGraph Theory -- Averaging in Time-Invariant Networks -- Averaging in Time-Varying Networks -- Performance and Robustness of Averaging Algorithms -- Averaging with Exogenous Inputs and Electrical Networks -- Index.
520	3		_aThis book deals with averaging dynamics, a paradigmatic example of network based dynamics in multi-agent systems. The book presents all the fundamental results on linear averaging dynamics, proposing a unified and updated viewpoint of many models and convergence results scattered in the literature. Starting from the classical evolution of the powers of a fixed stochastic matrix, the text then considers more general evolutions of products of a sequence of stochastic matrices, either deterministic or randomized. The theory needed for a full understanding of the models is constructed without assuming any knowledge of Markov chains or Perron-Frobenius theory. Jointly with their analysis of the convergence of averaging dynamics, theauthors derive the properties of stochastic matrices. These properties are related to the topological structure of the associated graph, which, in the book's perspective, represents the communication between agents. Special attention is paid to how these properties scale as the network grows in size. Finally, the understanding of stochastic matrices is applied to the study of other problems in multi-agent coordination: averaging with stubborn agents and estimation from relative measurements. The dynamics described in the book find application in the study of opinion dynamics in social networks, of information fusion in sensor networks, and of the collective motion of animal groups and teams of unmanned vehicles. Introduction to Averaging Dynamics over Networks will be of material interest to researchers in systems and control studying coordinated or distributed control, networked systems or multiagent systems and to graduate students pursuing courses in these areas.
988			_aEBSPRINGER_2018
650		4	_9400080 _aAplicaciones informáticas
700	1		_aFrasca, Paolo. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/307335495/
776	0	8	_iEdición impresa: _z9783319680217
776	0	8	_iEdición impresa: _z9783319680231
776	0	8	_iEdición impresa: _z9783319885322
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-68022-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE