Overview
- Introduces readers to key research issues in memristors, memristive devices and systems
- Includes chapters by eminent experts and pioneers in memristors such as Prof. Leon Chua (UC Berkeley, USA) and Dr. R.S. Williams (HP Labs, USA)
- Features special topics on memristors and memristive devices such as chaotic memristive systems, memristor emulators, and memristor oscillators
- Reports on the latest applications of memristors and memristive devices in science and engineering
- Illustrates key concepts with circuit designs and computer simulations
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 701)
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About this book
Throughout the book, special emphasis is given to papers offering practical solutions and design, modeling, and implementation insights to address current research problems in memristors, memristive devices and systems. As such, it offers a valuable reference book on memristors and memristive devices for graduate students and researchers with a basic knowledge of electrical and control systems engineering.
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Keywords
Table of contents (20 chapters)
Editors and Affiliations
Bibliographic Information
Book Title: Advances in Memristors, Memristive Devices and Systems
Editors: Sundarapandian Vaidyanathan, Christos Volos
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-51724-7
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-51723-0Published: 22 February 2017
Softcover ISBN: 978-3-319-84727-6Published: 18 July 2018
eBook ISBN: 978-3-319-51724-7Published: 15 February 2017
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XII, 511
Number of Illustrations: 65 b/w illustrations, 229 illustrations in colour
Topics: Computational Intelligence, Circuits and Systems, Electronics and Microelectronics, Instrumentation, Mathematical Models of Cognitive Processes and Neural Networks