001434959 000__ 04612cam\a22006257i\4500
001434959 001__ 1434959
001434959 003__ OCoLC
001434959 005__ 20230309003828.0
001434959 006__ m\\\\\o\\d\\\\\\\\
001434959 007__ cr\un\nnnunnun
001434959 008__ 210319s2021\\\\si\a\\\\ob\\\\001\0\eng\d
001434959 019__ $$a1243544560$$a1244119966$$a1244623174$$a1245666754$$a1246574332$$a1249105106$$a1249944174$$a1250095651
001434959 020__ $$a9789813349766$$q(electronic bk.)
001434959 020__ $$a981334976X$$q(electronic bk.)
001434959 020__ $$z9789813349759$$q(print)
001434959 020__ $$z9813349751$$q(print)
001434959 0247_ $$a10.1007/978-981-33-4976-6$$2doi
001434959 035__ $$aSP(OCoLC)1242407884
001434959 040__ $$aYDX$$beng$$erda$$cYDX$$dDCT$$dEBLCP$$dGW5XE$$dOCLCO$$dEMU$$dOCLCO$$dSFB$$dOCLCF$$dLEATE$$dUKAHL$$dOCLCO$$dOCLCQ
001434959 049__ $$aISEA
001434959 050_4 $$aQ325.5$$b.F54 2021
001434959 08204 $$a006.3$$223
001434959 24500 $$aFluctuation-induced network control and learning :$$bapplying the yuragi principle of brain and biological systems /$$cMasayuki Murata, Kenji Leibnitz, editors.
001434959 264_1 $$aSingapore :$$bSpringer,$$c[2021]
001434959 300__ $$a1 online resource (xi, 236 pages) :$$billustrations.
001434959 336__ $$atext$$btxt$$2rdacontent
001434959 337__ $$acomputer$$bc$$2rdamedia
001434959 338__ $$aonline resource$$bcr$$2rdacarrier
001434959 347__ $$atext file$$bPDF$$2rda
001434959 504__ $$aIncludes bibliographical references and index.
001434959 5050_ $$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.
001434959 506__ $$aAccess limited to authorized users.
001434959 520__ $$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.
001434959 588__ $$aDescription based on resource, viewed April 29, 2021.
001434959 650_0 $$aMachine learning.
001434959 650_0 $$aNeural networks (Computer science)
001434959 650_0 $$aArtificial intelligence.
001434959 650_0 $$aComputer networks.
001434959 650_0 $$aElectrical engineering.
001434959 650_6 $$aApprentissage automatique.
001434959 650_6 $$aRéseaux neuronaux (Informatique)
001434959 650_6 $$aIntelligence artificielle.
001434959 650_6 $$aRéseaux d'ordinateurs.
001434959 650_6 $$aGénie électrique.
001434959 655_0 $$aElectronic books.
001434959 7001_ $$aMurata, Masayuki,$$eeditor.
001434959 7001_ $$aLeibnitz, Kenji,$$eeditor.
001434959 7730_ $$tSpringer Nature eBook$$w(OCoLC-LEATE)288477
001434959 77608 $$iPrint version: $$z9813349751$$z9789813349759$$w(OCoLC)1206220730
001434959 852__ $$bebk
001434959 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-33-4976-6$$zOnline Access$$91397441.1
001434959 909CO $$ooai:library.usi.edu:1434959$$pGLOBAL_SET
001434959 980__ $$aBIB
001434959 980__ $$aEBOOK
001434959 982__ $$aEbook
001434959 983__ $$aOnline
001434959 994__ $$a92$$bISE