語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Reservoir Computing = Theory, Physic...
~
SpringerLink (Online service)
Reservoir Computing = Theory, Physical Implementations, and Applications /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Reservoir Computing/ edited by Kohei Nakajima, Ingo Fischer.
其他題名:
Theory, Physical Implementations, and Applications /
其他作者:
Fischer, Ingo.
面頁冊數:
XIX, 458 p. 161 illus., 127 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Quantum Information Technology, Spintronics. -
電子資源:
https://doi.org/10.1007/978-981-13-1687-6
ISBN:
9789811316876
Reservoir Computing = Theory, Physical Implementations, and Applications /
Reservoir Computing
Theory, Physical Implementations, and Applications /[electronic resource] :edited by Kohei Nakajima, Ingo Fischer. - 1st ed. 2021. - XIX, 458 p. 161 illus., 127 illus. in color.online resource. - Natural Computing Series. - Natural Computing Series.
Chapter 1: The cerebral cortex: A delay coupled recurrent oscillator network? -- Chapter 2: Cortico-Striatal Origins of Reservoir Computing, Mixed Selectivity and Higher Cognitive Function -- Chapter 3: Reservoirs learn to learn -- Chapter 4: Deep Reservoir Computing -- Chapter 5: On the characteristics and structures of dynamical systems suitable for reservoir computing -- Chapter 6: Reservoir Computing for Forecasting Large Spatiotemporal Dynamical Systems -- Chapter 7: Reservoir Computing in Material Substrates -- Chapter 8: Physical Reservoir Computing in Robotics -- Chapter 9: Reservoir Computing in MEMS -- Chapter 10: Neuromorphic Electronic Systems for Reservoir Computing -- Chapter 11: Reservoir Computing using Autonomous Boolean Networks Realized on Field-Programmable Gate Arrays -- Chapter 12: Programmable Fading Memory in Atomic Switch Systems for Error Checking Applications -- Chapter 13: Reservoir computing leveraging the transient non-linear dynamics of spin-torque nano-oscillators -- Chapter 14: Reservoir computing based on spintronics technology -- Chapter 15: Reservoir computing with dipole-coupled nanomagnets -- Chapter 16: Performance improvement of delay-based photonic reservoir computing -- Chapter 17: Computing with integrated photonic reservoirs -- Chapter 18: Quantum reservoir computing -- Chapter 19: Towards NMR Quantum Reservoir Computing.
This book is the first comprehensive book about reservoir computing (RC). RC is a powerful and broadly applicable computational framework based on recurrent neural networks. Its advantages lie in small training data set requirements, fast training, inherent memory and high flexibility for various hardware implementations. It originated from computational neuroscience and machine learning but has, in recent years, spread dramatically, and has been introduced into a wide variety of fields, including complex systems science, physics, material science, biological science, quantum machine learning, optical communication systems, and robotics. Reviewing the current state of the art and providing a concise guide to the field, this book introduces readers to its basic concepts, theory, techniques, physical implementations and applications. The book is sub-structured into two major parts: theory and physical implementations. Both parts consist of a compilation of chapters, authored by leading experts in their respective fields. The first part is devoted to theoretical developments of RC, extending the framework from the conventional recurrent neural network context to a more general dynamical systems context. With this broadened perspective, RC is not restricted to the area of machine learning but is being connected to a much wider class of systems. The second part of the book focuses on the utilization of physical dynamical systems as reservoirs, a framework referred to as physical reservoir computing. A variety of physical systems and substrates have already been suggested and used for the implementation of reservoir computing. Among these physical systems which cover a wide range of spatial and temporal scales, are mechanical and optical systems, nanomaterials, spintronics, and quantum many body systems. This book offers a valuable resource for researchers (Ph.D. students and experts alike) and practitioners working in the field of machine learning, artificial intelligence, robotics, neuromorphic computing, complex systems, and physics.
ISBN: 9789811316876
Standard No.: 10.1007/978-981-13-1687-6doiSubjects--Topical Terms:
783474
Quantum Information Technology, Spintronics.
LC Class. No.: Q334-342
Dewey Class. No.: 006.3
Reservoir Computing = Theory, Physical Implementations, and Applications /
LDR
:04819nam a22003975i 4500
001
1053133
003
DE-He213
005
20210817125651.0
007
cr nn 008mamaa
008
220103s2021 si | s |||| 0|eng d
020
$a
9789811316876
$9
978-981-13-1687-6
024
7
$a
10.1007/978-981-13-1687-6
$2
doi
035
$a
978-981-13-1687-6
050
4
$a
Q334-342
072
7
$a
UYQ
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
072
7
$a
UYQ
$2
thema
082
0 4
$a
006.3
$2
23
245
1 0
$a
Reservoir Computing
$h
[electronic resource] :
$b
Theory, Physical Implementations, and Applications /
$c
edited by Kohei Nakajima, Ingo Fischer.
250
$a
1st ed. 2021.
264
1
$a
Singapore :
$b
Springer Singapore :
$b
Imprint: Springer,
$c
2021.
300
$a
XIX, 458 p. 161 illus., 127 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
Natural Computing Series
505
0
$a
Chapter 1: The cerebral cortex: A delay coupled recurrent oscillator network? -- Chapter 2: Cortico-Striatal Origins of Reservoir Computing, Mixed Selectivity and Higher Cognitive Function -- Chapter 3: Reservoirs learn to learn -- Chapter 4: Deep Reservoir Computing -- Chapter 5: On the characteristics and structures of dynamical systems suitable for reservoir computing -- Chapter 6: Reservoir Computing for Forecasting Large Spatiotemporal Dynamical Systems -- Chapter 7: Reservoir Computing in Material Substrates -- Chapter 8: Physical Reservoir Computing in Robotics -- Chapter 9: Reservoir Computing in MEMS -- Chapter 10: Neuromorphic Electronic Systems for Reservoir Computing -- Chapter 11: Reservoir Computing using Autonomous Boolean Networks Realized on Field-Programmable Gate Arrays -- Chapter 12: Programmable Fading Memory in Atomic Switch Systems for Error Checking Applications -- Chapter 13: Reservoir computing leveraging the transient non-linear dynamics of spin-torque nano-oscillators -- Chapter 14: Reservoir computing based on spintronics technology -- Chapter 15: Reservoir computing with dipole-coupled nanomagnets -- Chapter 16: Performance improvement of delay-based photonic reservoir computing -- Chapter 17: Computing with integrated photonic reservoirs -- Chapter 18: Quantum reservoir computing -- Chapter 19: Towards NMR Quantum Reservoir Computing.
520
$a
This book is the first comprehensive book about reservoir computing (RC). RC is a powerful and broadly applicable computational framework based on recurrent neural networks. Its advantages lie in small training data set requirements, fast training, inherent memory and high flexibility for various hardware implementations. It originated from computational neuroscience and machine learning but has, in recent years, spread dramatically, and has been introduced into a wide variety of fields, including complex systems science, physics, material science, biological science, quantum machine learning, optical communication systems, and robotics. Reviewing the current state of the art and providing a concise guide to the field, this book introduces readers to its basic concepts, theory, techniques, physical implementations and applications. The book is sub-structured into two major parts: theory and physical implementations. Both parts consist of a compilation of chapters, authored by leading experts in their respective fields. The first part is devoted to theoretical developments of RC, extending the framework from the conventional recurrent neural network context to a more general dynamical systems context. With this broadened perspective, RC is not restricted to the area of machine learning but is being connected to a much wider class of systems. The second part of the book focuses on the utilization of physical dynamical systems as reservoirs, a framework referred to as physical reservoir computing. A variety of physical systems and substrates have already been suggested and used for the implementation of reservoir computing. Among these physical systems which cover a wide range of spatial and temporal scales, are mechanical and optical systems, nanomaterials, spintronics, and quantum many body systems. This book offers a valuable resource for researchers (Ph.D. students and experts alike) and practitioners working in the field of machine learning, artificial intelligence, robotics, neuromorphic computing, complex systems, and physics.
650
2 4
$a
Quantum Information Technology, Spintronics.
$3
783474
650
2 4
$a
Mathematical Models of Cognitive Processes and Neural Networks.
$3
884110
650
2 4
$a
Machine Learning.
$3
1137723
650
2 4
$a
Control, Robotics, Mechatronics.
$3
768396
650
1 4
$a
Artificial Intelligence.
$3
646849
650
0
$a
Spintronics.
$3
580491
650
0
$a
Quantum computers.
$3
564139
650
0
$a
Neural networks (Computer science) .
$3
1253765
650
0
$a
Machine learning.
$3
561253
650
0
$a
Mechatronics.
$3
559133
650
0
$a
Robotics.
$3
561941
650
0
$a
Control engineering.
$3
1249728
650
0
$a
Artificial intelligence.
$3
559380
700
1
$a
Fischer, Ingo.
$e
editor.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1357952
700
1
$a
Nakajima, Kohei.
$e
editor.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1357951
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9789811316869
776
0 8
$i
Printed edition:
$z
9789811316883
830
0
$a
Natural Computing Series
$3
1354249
856
4 0
$u
https://doi.org/10.1007/978-981-13-1687-6
912
$a
ZDB-2-SCS
912
$a
ZDB-2-SXCS
950
$a
Computer Science (SpringerNature-11645)
950
$a
Computer Science (R0) (SpringerNature-43710)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入