Reservoir Computing / Natural Computing Series (PDF)
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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 byleading 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.
Ingo Fischer has since 2009 been a Research Professor of the Spanish National Research Council (CSIC) at the Institute for Cross-Disciplinary Physics and ComplexSystems IFISC (UIB-CSIC) in Palma de Mallorca (Spain). Moreover, he is head of the University group 'Experimental Physics of Complex Systems' at the Universitat de les Illes Balears (UIB) and currently Deputy Scientific Director of the Maria de Maeztu Unit of Excellence on 'Information Processing in and by Complex Systems'. His research has been covering nonlinear photonics, brain-inspired information processing, in particular reservoir computing, complex systems and their applications, broad area semiconductor lasers and quantum chaos. He received his diploma and Ph.D. degrees in physics from Philipps-University Marburg (Germany) in 1992 and 1995, respectively. He was a Post-doctoral researcher at Advanced Telecommunication Research Labs at Kyoto (Japan), was Hochschul-Assistent (Assistant Professor) at TU Darmstadt (Germany), senior visiting scientist at Vrije Universiteit Brussel (Belgium), and from 2007 to 2009 full professor (chair) for photonics and integrated systems at Heriot-Watt
- 2021, 1st ed. 2021, 458 Seiten, Englisch
- Herausgegeben: Kohei Nakajima, Ingo Fischer
- Verlag: Springer Nature Singapore
- ISBN-10: 9811316872
- ISBN-13: 9789811316876
- Erscheinungsdatum: 05.08.2021
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- Grösse: 20 MB
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