國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Fluctuation-induced network control ...

Leibnitz, Kenji.

Fluctuation-induced network control and learningapplying the Yuragi principle of brain and biological systems /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fluctuation-induced network control and learningedited by Masayuki Murata, Kenji Leibnitz.
其他題名:	applying the Yuragi principle of brain and biological systems /
其他作者:	Murata, Masayuki.
出版者:	Singapore :Springer Singapore :2021.
面頁冊數:	xi, 236 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-981-33-4976-6
ISBN:	9789813349766$q(electronic bk.)

Fluctuation-induced network control and learningapplying the Yuragi principle of brain and biological systems /
Fluctuation-induced network control and learningapplying the Yuragi principle of brain and biological systems /[electronic resource] :edited by Masayuki Murata, Kenji Leibnitz. - Singapore :Springer Singapore :2021. - xi, 236 p. :ill., digital ;24 cm.

Chapter 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.

From 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.

ISBN: 9789813349766$q(electronic bk.)

Standard No.: 10.1007/978-981-33-4976-6doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .F58 2021

Dewey Class. No.: 006.31

Fluctuation-induced network control and learningapplying the Yuragi principle of brain and biological systems /
LDR:03303nmm a2200325 a 4500 001 599277
003 DE-He213
005 20210316062716.0
006 m d
007 cr nn 008maaau
008 211027s2021 si s 0 eng d
020 $a 9789813349766$q(electronic bk.)
020 $a 9789813349759$q(paper)
024 7 $a 10.1007/978-981-33-4976-6 $2 doi
035 $a 978-981-33-4976-6
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .F58 2021
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .F646 2021
245 0 0 $a Fluctuation-induced network control and learning $h [electronic resource] : $b applying the Yuragi principle of brain and biological systems / $c edited by Masayuki Murata, Kenji Leibnitz.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a xi, 236 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 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 $a From 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.
650 0 $a Machine learning. $3 188639
650 0 $a Computer networks. $3 181923
650 0 $a Natural computation. $3 339160
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Computer Communication Networks. $3 218087
650 2 4 $a Communications Engineering, Networks. $3 273745
700 1 $a Murata, Masayuki. $3 346438
700 1 $a Leibnitz, Kenji. $3 893414
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-33-4976-6
950 $a Computer Science (SpringerNature-11645)