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Books > Science & Mathematics > Physics > Quantum physics (quantum mechanics)
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Reservoir Computing - Theory, Physical Implementations, and Applications (Hardcover, 1st ed. 2021)
Loot Price: R4,660
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Reservoir Computing - Theory, Physical Implementations, and Applications (Hardcover, 1st ed. 2021)
Series: Natural Computing Series
Expected to ship within 10 - 15 working days
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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 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.
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