| 书目名称 | Reservoir Computing | | 副标题 | Theory, Physical Imp | | 编辑 | Kohei Nakajima,Ingo Fischer | | 视频video | | | 概述 | The first comprehensive book on reservoir computing.Provides an introduction and cutting-edge research in a wide range of domains.Contributed by leading researchers in the field | | 丛书名称 | Natural Computing Series | | 图书封面 |  | | 描述 | .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 dynamic | | 出版日期 | Book 2021 | | 关键词 | Reservoir Computing; Neural Networks; Machine Learning; Soft Robotics; Signal Processing; dynamical syste | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-13-1687-6 | | isbn_ebook | 978-981-13-1687-6Series ISSN 1619-7127 Series E-ISSN 2627-6461 | | issn_series | 1619-7127 | | copyright | Springer Nature Singapore Pte Ltd. 2021 |
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发表于 2025-3-21 18:39:43
