Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications
- 1st Edition - June 17, 2021
- Imprint: Academic Press
- Editors: Christos Volos, Viet-Thanh Pham
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 1 1 8 4 - 7
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 3 2 0 2 - 6
Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications illustrates recent advances in the field of mem-elements (memristor, memcapacitor, meminduct… Read more
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Request a sales quoteMem-elements for Neuromorphic Circuits with Artificial Intelligence Applications illustrates recent advances in the field of mem-elements (memristor, memcapacitor, meminductor) and their applications in nonlinear dynamical systems, computer science, analog and digital systems, and in neuromorphic circuits and artificial intelligence. The book is mainly devoted to recent results, critical aspects and perspectives of ongoing research on relevant topics, all involving networks of mem-elements devices in diverse applications. Sections contribute to the discussion of memristive materials and transport mechanisms, presenting various types of physical structures that can be fabricated to realize mem-elements in integrated circuits and device modeling.
As the last decade has seen an increasing interest in recent advances in mem-elements and their applications in neuromorphic circuits and artificial intelligence, this book will attract researchers in various fields.
- Covers a broad range of interdisciplinary topics between mathematics, circuits, realizations, and practical applications related to nonlinear dynamical systems, nanotechnology, analog and digital systems, computer science and artificial intelligence
- Presents recent advances in the field of mem-elements (memristor, memcapacitor, meminductor)
- Includes interesting applications of mem-elements in nonlinear dynamical systems, analog and digital systems, neuromorphic circuits, computer science and artificial intelligence
Researchers from academia and industry, who are working in the research areas—dynamical systems, artificial intelligence, electrical engineering, computer science, information technology and neuromorphic systems .
Graduate or advanced undergraduate level as a textbook or major reference for courses such as electrical circuits, nonlinear dynamical systems, mathematical modelling, computational science, numerical simulation, and many others.
Graduate students and young researchers in the following fields: Biomedical Engineering; Computational biology; Computer science; Computational Physics; Engineering Mathematics, Electrical and Electronic Engineering; Mathematics; Physics.
Graduate or advanced undergraduate level as a textbook or major reference for courses such as electrical circuits, nonlinear dynamical systems, mathematical modelling, computational science, numerical simulation, and many others.
Graduate students and young researchers in the following fields: Biomedical Engineering; Computational biology; Computer science; Computational Physics; Engineering Mathematics, Electrical and Electronic Engineering; Mathematics; Physics.
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Preface
- Part I: Mem-elements and their emulators
- Chapter 1: The fourth circuit element was found: a brief history
- Abstract
- 1.1. Memristor – the first step
- 1.2. Properties of memristor
- 1.3. Memristive systems
- 1.4. The first physical model of the memristor
- 1.5. Memristor's applications
- 1.6. Conclusion
- References
- Chapter 2: Implementing memristor emulators in hardware
- Abstract
- 2.1. Introduction
- 2.2. Memristor modeling framework
- 2.3. The fingerprints of a memristor
- 2.4. Designing memristor emulators
- 2.5. Conclusion
- References
- Chapter 3: On the FPGA implementation of chaotic oscillators based on memristive circuits
- Abstract
- 3.1. Introduction
- 3.2. 3D, 4D, and 5D memristive systems
- 3.3. Numerical methods
- 3.4. Analysis of memristive-based chaotic oscillators
- 3.5. FPGA implementation of memristive systems
- 3.6. Memristive-based secure communication system
- 3.7. Conclusions
- References
- Chapter 4: Microwave memristive components for smart RF front-end modules
- Abstract
- Acknowledgements
- 4.1. RF/microwave model of memristive switch and PIN diode
- 4.2. Memristive phase-shifter realization
- 4.3. Reconfigurable dual-band bandpass microwave filter
- 4.4. Dual-band bandpass filter with multilayer dual-mode resonator enhanced with RF memristor
- References
- Chapter 5: The modeling of memcapacitor oscillator motion with ANN and its nonlinear control application
- Abstract
- 5.1. Introduction
- 5.2. Chaotic memcapacitor oscillator and its dynamical analysis
- 5.3. Nonlinear feedback control
- 5.4. Chaotic motion extraction from video and ANN
- 5.5. Identification of memcapacitor system with ANN
- 5.6. Conclusions
- References
- Chapter 6: Rich dynamics of memristor based Liénard systems
- Abstract
- Acknowledgement
- 6.1. Introduction
- 6.2. Model system
- 6.3. Mixed-mode oscillations
- 6.4. Higher dimensional torus and large expanded chaotic attractor
- 6.5. Conclusion
- References
- Chapter 7: Hidden extreme multistability generated from a novel memristive two-scroll chaotic system
- Abstract
- 7.1. Introduction
- 7.2. Memristive two-scroll chaotic system and its basic properties
- 7.3. Dynamical analysis of memristive two-scroll chaotic system
- 7.4. Circuit design and experimental measurements
- 7.5. Conclusion
- References
- Chapter 8: Extreme multistability, hidden chaotic attractors and amplitude controls in an absolute memristor Van der Pol–Duffing circuit: dynamical analysis and electronic implementation
- Abstract
- 8.1. Introduction
- 8.2. Theoretical analysis of an absolute memristor autonomous Van der Pol–Duffing circuit
- 8.3. Electronic implementation of an absolute memristor autonomous Van der Pol–Duffing circuit
- 8.4. Conclusion
- References
- Chapter 9: Memristor-based novel 4D chaotic system without equilibria
- Abstract
- 9.1. Introduction
- 9.2. Brief introduction to flux- and charge-controlled memristor models and novel chaotic system
- 9.3. Properties and behaviors of memristor-based novel chaotic system
- 9.4. Projective synchronization between the memristor-based chaotic systems
- 9.5. Simulation results and discussion
- 9.6. Conclusions and future scope
- References
- Chapter 10: Memristor Helmholtz oscillator: analysis, electronic implementation, synchronization and chaos control using single controller
- Abstract
- 10.1. Introduction
- 10.2. Design and analysis of the proposed memristor Helmholtz oscillator
- 10.3. Electronic circuit simulations of the proposed memristor Helmholtz oscillator
- 10.4. Chaos synchronization of unidirectional coupled identical chaotic memristor Helmholtz oscillators
- 10.5. Chaos control of memristor Helmholtz oscillator using single controller
- 10.6. Conclusion
- References
- Chapter 11: Design guidelines for physical implementation of fractional-order integrators and its application in memristive systems
- Abstract
- Acknowledgements
- 11.1. Introduction
- 11.2. Fractional-order calculus preliminaries
- 11.3. Fractional-order memristive systems
- 11.4. Continued fraction expansion (CFE)
- 11.5. Implementation of fractional-order integrators using FPAAs
- 11.6. Electronic implementation of a fractional-order memristive system
- 11.7. Conclusions
- References
- Chapter 12: Control of bursting oscillations in memristor based Wien-bridge oscillator
- Abstract
- Acknowledgements
- 12.1. Introduction
- 12.2. Mathematical model of LC network based diode bridge memristor
- 12.3. Memristive Wien-bridge oscillator
- 12.4. Chaotic and periodic bursting oscillations (BOs)
- 12.5. Control of active states and quiescent states in BOs
- 12.6. Control of amplitude in BOs
- 12.7. Conclusion
- References
- Part II: Applications of mem-elements
- Chapter 13: Memristor, mem-systems and neuromorphic applications: a review
- Abstract
- Acknowledgement
- 13.1. Introduction
- 13.2. Memristor and mem-systems
- 13.3. Neuromorphic systems
- 13.4. Reservoir computing
- 13.5. Conclusion
- References
- Chapter 14: Guidelines for benchmarking non-ideal analog memristive crossbars for neural networks
- Abstract
- 14.1. Introduction
- 14.2. Basic concepts
- 14.3. Non-idealities of memristors
- 14.4. Applications
- 14.5. Conclusions
- References
- Chapter 15: Bipolar resistive switching in biomaterials: case studies of DNA and melanin-based bio-memristive devices
- Abstract
- Acknowledgements
- 15.1. Introduction
- 15.2. Brief overview of resistive switching and memristive devices
- 15.3. Materials for resistive switching application
- 15.4. Biomaterial-based memristive devices
- 15.5. Conclusion and future outlook
- References
- Chapter 16: Nonvolatile memristive logic: a road to in-memory computing
- Abstract
- Acknowledgements
- 16.1. Introduction
- 16.2. Memristive logic gates in crossbar array
- 16.3. R-R logic gate
- 16.4. Memristive V-R logic
- 16.5. Challenges and outlooks
- References
- Chapter 17: Implementation of organic RRAM with ink-jet printer: from design to using in RFID-based application
- Abstract
- 17.1. Introduction
- 17.2. Design process
- 17.3. Fabrication process
- 17.4. A practical application example
- 17.5. Conclusion
- References
- Chapter 18: Neuromorphic vision networks for face recognition
- Abstract
- 18.1. Introduction
- 18.2. Preliminaries
- 18.3. Model description
- 18.4. Template formation
- 18.5. Face recognition
- 18.6. Memristive threshold logic (MTL)
- 18.7. Edge detection with memristive threshold logic (MTL) cells
- 18.8. Circuit realization
- 18.9. Experimental setup
- 18.10. Results and discussion
- 18.11. Future research directions
- 18.12. Conclusion
- 18.13. Key terms and definitions
- References
- Chapter 19: Synaptic devices based on HfO2 memristors
- Abstract
- Acknowledgements
- 19.1. Introduction
- 19.2. HfO2-based resistive switching structures
- 19.3. Demonstration of learning rules in memristor devices to mimic biological synapses
- 19.4. Stability and reliability issues of resistive synaptic devices
- 19.5. Physical simulation of memristors
- 19.6. Memristor compact modeling
- 19.7. Memristor random telegraph noise
- 19.8. Conclusion
- References
- Chapter 20: Analog circuit integration of backpropagation learning in memristive HTM architecture
- Abstract
- 20.1. Introduction
- 20.2. What is HTM?
- 20.3. Memristive HTM architectures
- 20.4. Analog backpropagation circuit integration in memristive HTM
- 20.5. Discussion and open problems
- 20.6. Conclusions
- References
- Chapter 21: Multi-stable patterns coexisting in memristor synapse-coupled Hopfield neural network
- Abstract
- Acknowledgement
- 21.1. Introduction
- 21.2. Memristor synapse-coupled HNN with three neurons
- 21.3. Bifurcation behaviors with multi-stability
- 21.4. Circuit synthesis and PSIM simulation
- 21.5. Conclusion
- References
- Chapter 22: Fuzzy memristive networks
- Abstract
- 22.1. Introduction
- 22.2. Requirement
- 22.3. Memristive fuzzy logic systems
- 22.4. Delay memristive fuzzy systems
- 22.5. Fractional memristive fuzzy systems
- 22.6. General overview of the area and future trends
- References
- Chapter 23: Fuzzy integral sliding mode technique for synchronization of memristive neural networks
- Abstract
- 23.1. Introduction
- 23.2. Model description
- 23.3. Controller design
- 23.4. Numerical results
- 23.5. Conclusions
- References
- Chapter 24: Robust adaptive control of fractional-order memristive neural networks
- Abstract
- 24.1. Introduction
- 24.2. Model of the system and preliminary concepts
- 24.3. Controller design
- 24.4. Simulation results
- 24.5. Conclusion
- References
- Chapter 25: Learning memristive spiking neurons and beyond
- Abstract
- 25.1. Introduction
- 25.2. Spike domain data processing and learning
- 25.3. Learning in memristive neuromorphic architectures
- 25.4. Open problems and research directions
- 25.5. Conclusion
- References
- Index
- Edition: 1
- Published: June 17, 2021
- No. of pages (Paperback): 568
- No. of pages (eBook): 568
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780128211847
- eBook ISBN: 9780128232026
CV
Christos Volos
Christos Volos received the Physics Diploma degree, the M.Sc. degree in electronics, and the Ph.D. degree in chaotic electronics from the Physics Department, Aristotle University of Thessaloniki, in 1999, 2002, and 2008, respectively. He is currently an Associate Professor with the Physics Department, Aristotle University of Thessaloniki, Greece and a member of the Laboratory of Nonlinear Circuits – Systems & Complexity (LaNSCom).
Affiliations and expertise
Associate Professor, Aristotle University of Thessaloniki, GreeceVP
Viet-Thanh Pham
Viet-Thanh Pham is the Director of Research at Faculty of Electrical and Electronic Engineering, Phenikaa Institute for Advanced Study (PIAS), Phenikaa University, Vietnam. He received the degree in electronics and telecommunications from the Hanoi University of Technology, Vietnam, in 2005, and the Ph.D. degree in electronics, automation and control of complex systems engineering from the University of Catania, Italy, in 2013. He was a postdoctoral researcher at the Division of Dynamics, Lodz University of Technology, Poland.
Affiliations and expertise
Director of Research, Phenikaa Institute for Advanced Study (PIAS), Phenikaa University, VietnamRead Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications on ScienceDirect