Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications

Christos Volos, Viet-Thanh Pham (Herausgeber)

Buch | Softcover

568 Seiten

2021
Academic Press Inc (Verlag)
978-0-12-821184-7 (ISBN)

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Mem-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.

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). 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.

Part I: Mem-elements and their emulators

1. The fourth circuit element was found: a brief history
2. Implementing memristor emulators in hardware
3. On the FPGA implementation of chaotic oscillators based on memristive circuits
4. Microwave memristive components for smart RF front-end modules
5. The modeling of memcapacitor oscillator motion with ANN and its nonlinear control application
6. Rich dynamics of memristor based Liénard systems
7. Hidden extreme multistability generated from a novel memristive two-scroll chaotic system
8. Extreme multistability, hidden chaotic attractors and amplitude controls in an absolute memristor Van der Pol–Duffing circuit: dynamical analysis and electronic implementation
9. Memristor-based novel 4D chaotic system without equilibria
10. Memristor Helmholtz oscillator: analysis, electronic implementation, synchronization and chaos control using single controller
11. Design guidelines for physical implementation of fractional-order integrators and its application in memristive systems
12. Control of bursting oscillations in memristor based Wien-bridge oscillator

Part II: Applications of mem-elements

13. Memristor, mem-systems and neuromorphic applications: a review
14. Guidelines for benchmarking non-ideal analog memristive crossbars for neural networks
15. Bipolar resistive switching in biomaterials: case studies of DNA and melanin-based bio-memristive devices
16. Nonvolatile memristive logic: a road to in-memory computing
17. Implementation of organic RRAM with ink-jet printer: from design to using in RFID-based application
18. Neuromorphic vision networks for face recognition
19. Synaptic devices based on HfO2 memristors
20. Analog circuit integration of backpropagation learning in memristive HTM architecture
21. Multi-stable patterns coexisting in memristor synapse-coupled Hopfield neural network
22. Fuzzy memristive networks
23. Fuzzy integral sliding mode technique for synchronization of memristive neural networks
24. Robust adaptive control of fractional-order memristive neural networks
25. Learning memristive spiking neurons and beyond

Erscheinungsdatum	02.07.2021
Reihe/Serie	Advances in Nonlinear Dynamical Systems and Robotics (ANDC)
Verlagsort	San Diego
Sprache	englisch
Maße	152 x 229 mm
Gewicht	880 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Informatik ► Weitere Themen ► CAD-Programme
	Technik ► Architektur
	Technik ► Maschinenbau
ISBN-10	0-12-821184-9 / 0128211849
ISBN-13	978-0-12-821184-7 / 9780128211847
Zustand	Neuware