000			03970nam a2200409 i 4500
999			_c334629 _d334629 _x1
001			334629
003			ES-MaUEC
005			20230102114730.0
006			a|||| o|||| 00| 0
007			cr nn nnnaamaa
008			210316s2021 si a o |||| 0|eng d
020			_a9789813349766
024	7		_a10.1007/978-981-33-4976-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
245	0	0	_aFluctuation-induced network control and learning : _bapplying the yuragi principle of brain and biological systems _cedited by Masayuki Murata, Kenji Leibnitz
250			_aFirst edition 2021
264		1	_aSingapore _bSpringer International Publising _c2021
300			_a1 recurso en línea (XI, 236 páginas) _b104 ilustraciones, 86 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
505	0		_aChapter 1: Introduction to Yuragi Theory and Yuragi Control -- Chapter 2: Functional Roles of Yuragi in Biosystems -- Chapter 3: Next-Generation Bio- and Brain-Inspired Networking -- Chapter 4: Yuragi-Based Virtual Network Control -- Chapter 5: Introduction to Yuragi Learning -- Chapter 6: Fast/Slow-Pathway Bayesian Attractor Model for IoT Networks Based on Software-Defined Networking with Virtual Network Slicing -- Chapter 7: Application to IoT Network Control -- Chapter 8: Another Prediction Method and Application to Low-Power Wide-Area Networks -- Chapter 9: Artificial Intelligence Platform for Yuragi Learning -- Chapter 10: Bias-Free Yuragi Learning.
520	3		_aFrom theory to application, this book presents research on biologically and brain-inspired networking and machine learning based on Yuragi, which is the Japanese term describing the noise or fluctuations that are inherently used to control the dynamics of a system. The Yuragi mechanism can be found in various biological contexts, such as in gene expression dynamics, molecular motors in muscles, or the visual recognition process in the brain. Unlike conventional network protocols that are usually designed to operate under controlled conditions with a predefined set of rules, the probabilistic behavior of Yuragi-based control permits the system to adapt to unknown situations in a distributed and self-organized manner leading to a higher scalability and robustness. The book consists of two parts. Part 1 provides in four chapters an introduction to the biological background of the Yuragi concept as well as how these are applied to networking problems. Part 2 provides additional contributions that extend the original Yuragi concept to a Bayesian attractor model from human perceptual decision making. In the six chapters of the second part, applications to various fields in information network control and artificial intelligence are presented, ranging from virtual network reconfigurations, a software-defined Internet of Things, and low-power wide-area networks. This book will benefit those working in the fields of information networks, distributed systems, and machine learning who seek new design mechanisms for controlling large-scale dynamically changing systems.
988			_aSpringer_Computer_2021
650		7	_2embne _aAprendizaje automático _9166090
650		7	_2embne _aRedes neuronales artificiales _9678664
700	1		_aMurata, Masayuki _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aLeibnitz, Kenji _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-33-4976-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2021 _dz _eb _zSI