Deep Learning Classifiers with Memristive Networks Theory and Applic
This book introduces readers to the fundamentals of deep neural network architectures, with a special emphasis on memristor circuits and systems. At first, the book offers an overview of neuro-memristive systems, including memristor devices, models, and t
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Alex Pappachen James Editor
Deep Learning Classifiers with Memristive Networks Theory and Applications
Modeling and Optimization in Science and Technologies Volume 14
Series Editors Srikanta Patnaik, SOA University, Bhubaneswar, India e-mail: [email protected] Ishwar K. Sethi, Oakland University, Rochester, USA e-mail: [email protected] Xiaolong Li, Indiana State University, Terre Haute, USA e-mail: [email protected] Editorial Board Li Cheng, The Hong Kong Polytechnic University, Hong Kong Jeng-Haur Horng, National Formosa University, Yulin, Taiwan Pedro U. Lima, Institute for Systems and Robotics, Lisbon, Portugal Mun-Kew Leong, Institute of Systems Science, National University of Singapore, Singapore Muhammad Nur, Diponegoro University, Semarang, Indonesia Luca Oneto, University of Genoa, Italy Kay Chen Tan, National University of Singapore, Singapore Sarma Yadavalli, University of Pretoria, South Africa Yeon-Mo Yang, Kumoh National Institute of Technology, Gumi, Korea (Republic of) Liangchi Zhang, The University of New South Wales, Australia Baojiang Zhong, Soochow University, Suzhou, China Ahmed Zobaa, Brunel University, Uxbridge, Middlesex, UK
The book series Modeling and Optimization in Science and Technologies (MOST) publishes basic principles as well as novel theories and methods in the fast-evolving field of modeling and optimization. Topics of interest include, but are not limited to: methods for analysis, design and control of complex systems, networks and machines; methods for analysis, visualization and management of large data sets; use of supercomputers for modeling complex systems; digital signal processing; molecular modeling; and tools and software solutions for different scientific and technological purposes. Special emphasis is given to publications discussing novel theories and practical solutions that, by overcoming the limitations of traditional methods, may successfully address modern scientific challenges, thus promoting scientific and technological progress. The series publishes monographs, contributed volumes and conference proceedings, as well as advanced textbooks. The main targets of the series are graduate students, researchers and professionals working at the forefront of their fields. Indexed by SCOPUS. The books of the series are submitted for indexing to Web of Science.
More information about this series at http://www.springer.com/series/10577
Alex Pappachen James Editor
Deep Learning Classifiers with Memristive Networks Theory and Applications
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Editor Alex Pappachen James School of Engineering Nazarbayev University Astana, Kazakhstan
ISSN 2196-7326 ISSN 2196-7334 (electronic) Modeling and Optimization in Science and Technologies ISBN 978-3-030-14522-4 ISBN 978-3-030-14524-8 (eBook) https://doi.org/10.1007/978-3-030-14524-8 Library of Congress Control Number: 2019932719 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically
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