دانلود کتاب Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications (Advances in Nonlinear Dynamics and Chaos (ANDC))

فهرست مطالب :

Front Cover
Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications
Copyright
Contents
List of contributors
Preface
Part 1 Mem-elements and their emulators
1 The fourth circuit element was found: a brief history
1.1 Memristor – the first step
1.2 Properties of memristor
1.2.1 Passivity criterion
1.2.2 Closure theorem
1.2.3 Existence and uniqueness theorem
1.3 Memristive systems
1.4 The first physical model of the memristor
1.5 Memristor\'s applications
1.6 Conclusion
References
2 Implementing memristor emulators in hardware
2.1 Introduction
2.2 Memristor modeling framework
2.3 The fingerprints of a memristor
2.3.1 The classical requirements: explicit conditions
2.3.2 Additional requirements: implicit conditions
2.4 Designing memristor emulators
2.4.1 The diode bridge
2.4.2 A short-term memory emulator
2.4.2.1 Nonlinear resistive two-port for memristor implementation
2.4.3 A long-term memory emulator
2.4.3.1 Circuit description and operation
2.4.3.2 Physical layout
2.4.3.3 Results
2.5 Conclusion
References
3 On the FPGA implementation of chaotic oscillators based on memristive circuits
3.1 Introduction
3.2 3D, 4D, and 5D memristive systems
3.3 Numerical methods
3.3.1 One-step methods
3.3.2 Multi-step methods
3.3.3 Stability of the 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
4 Microwave memristive components for smart RF front-end modules
4.1 RF/microwave model of memristive switch and PIN diode
4.2 Memristive phase-shifter realization
4.2.1 Planar main-line memristor mounted type loaded-line phase shifter
4.2.2 Implementation of main-line memristor mounted type loaded-line phase shifter and results
4.3 Reconfigurable dual-band bandpass microwave filter
4.4 Dual-band bandpass filter with multilayer dual-mode resonator enhanced with RF memristor
4.4.1 Dual-mode resonator with memristor
4.4.2 Dual-band bandpass filter with multilayer dual-mode resonator enhanced with RF memristor
4.4.3 Conclusion
Acknowledgments
References
5 The modeling of memcapacitor oscillator motion with ANN and its nonlinear control application
5.1 Introduction
5.2 Chaotic memcapacitor oscillator and its dynamical analysis
5.2.1 Equilibrium points
5.2.2 Bifurcation analysis
5.3 Nonlinear feedback control
5.4 Chaotic motion extraction from video and ANN
5.4.1 Perceptron architecture
5.4.2 Multilayer artificial neural networks
5.4.3 Delayed artificial neural networks
5.4.4 Training artificial neural networks
5.5 Identification of memcapacitor system with ANN
5.6 Conclusions
References
6 Rich dynamics of memristor based Liénard systems
6.1 Introduction
6.2 Model system
6.2.1 Stability analysis
6.2.2 Hidden attractors
6.3 Mixed-mode oscillations
6.3.1 Frequency scanning
6.3.2 Amplitude scanning
6.3.3 Successive period-adding sequence of MMOs
6.4 Higher dimensional torus and large expanded chaotic attractor
6.5 Conclusion
Acknowledgment
References
7 Hidden extreme multistability generated from a novel memristive two-scroll chaotic system
7.1 Introduction
7.2 Memristive two-scroll chaotic system and its basic properties
7.3 Dynamical analysis of memristive two-scroll chaotic system
7.3.1 Two-parameter Lyapunov exponents analysis
7.3.2 One parameter bifurcation analysis
7.3.3 Emergence of hidden extreme multistability
7.3.4 Remerging period-doubling bifurcation
7.3.5 Offset boosting control
7.4 Circuit design and experimental measurements
7.5 Conclusion
References
8 Extreme multistability, hidden chaotic attractors and amplitude controls in an absolute memristor Van der Pol–Duffing circuit: dynamical analysis and electronic implementation
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
9 Memristor-based novel 4D chaotic system without equilibria
9.1 Introduction
9.1.1 Literature survey
9.1.2 Application of memristor and memristive circuit
9.2 Brief introduction to flux- and charge-controlled memristor models and novel chaotic system
9.2.1 Introduction to flux- and charge-controlled memristor models
9.2.2 Flux-controlled memristor-based novel chaotic system
9.3 Properties and behaviors of memristor-based novel chaotic system
9.3.1 Symmetry and invariance
9.3.2 Dissipation
9.3.3 Lyapunov spectrum
9.3.4 Bifurcation diagram
9.3.5 Kaplan–Yorke dimension
9.3.6 Poincaré section
9.4 Projective synchronization between the memristor-based chaotic systems
9.5 Simulation results and discussion
9.6 Conclusions and future scope
References
10 Memristor Helmholtz oscillator: analysis, electronic implementation, synchronization and chaos control using single controller
10.1 Introduction
10.2 Design and analysis of the proposed memristor Helmholtz oscillator
10.2.1 Equilibrium points and their stabilities
10.2.2 Dynamical analysis of 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
11 Design guidelines for physical implementation of fractional-order integrators and its application in memristive systems
11.1 Introduction
11.2 Fractional-order calculus preliminaries
11.2.1 Grünwald–Letnikov definition
11.3 Fractional-order memristive systems
11.4 Continued fraction expansion (CFE)
11.4.1 CFE error analysis
11.5 Implementation of fractional-order integrators using FPAAs
11.5.1 Fractional-order integrator based on a first order transfer function
11.6 Electronic implementation of a fractional-order memristive system
11.7 Conclusions
Acknowledgments
References
12 Control of bursting oscillations in memristor based Wien-bridge oscillator
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
12.6.1 Amplitude increasing in BOs
12.6.2 Amplitude decreasing in BOs
12.7 Conclusion
Acknowledgments
References
Part 2 Applications of mem-elements
13 Memristor, mem-systems and neuromorphic applications: a review
13.1 Introduction
13.2 Memristor and mem-systems
13.2.1 Memristor
13.2.2 Memristive systems
13.3 Neuromorphic systems
13.3.1 Neuron model
13.3.2 Synapse
13.3.3 Neural network
13.4 Reservoir computing
13.5 Conclusion
Acknowledgment
References
14 Guidelines for benchmarking non-ideal analog memristive crossbars for neural networks
14.1 Introduction
14.2 Basic concepts
14.2.1 Memristor simplified
14.2.2 Crossbar simplified
14.2.3 Analog neural networks simplified
14.3 Non-idealities of memristors
14.3.1 Aging
14.3.2 Electromagnetics and signal integrity
14.3.3 Conductance variabilities
14.3.4 Noise effects
14.3.5 System implementations
14.4 Applications
14.4.1 Programmable logic
14.4.2 Neuromorphic accelerators
14.5 Conclusions
References
15 Bipolar resistive switching in biomaterials: case studies of DNA and melanin-based bio-memristive devices
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
15.4.1 Case study 1: DNA-based memristive device
15.4.1.1 Materials and methods
15.4.1.2 Results and discussion
15.4.1.3 Summary
15.4.2 Case study 2: melanin-based memristive device
15.4.2.1 Materials and methods
15.4.2.2 Results and discussion
15.4.2.3 Summary
15.5 Conclusion and future outlook
Acknowledgments
References
16 Nonvolatile memristive logic: a road to in-memory computing
16.1 Introduction
16.2 Memristive logic gates in crossbar array
16.3 R-R logic gate
16.3.1 Material implication logic (IMPLY or IMP)
16.3.2 Variants of IMP — NOR logic gate
16.3.3 Variants of IMP – NAND and AND logic gates
16.3.4 Memristor-aided logic (MAGIC)
16.3.5 Hyperdimensional computing in 3D RRAM
16.3.6 R-R logic based on neural networks
16.3.7 Conclusion of memristive R-R logic
16.4 Memristive V-R logic
16.4.1 Memristive sequential logic based on a single device
16.4.2 Four-variables methods
16.4.2.1 A four-variable method using a single BRS in a crossbar array
16.4.2.2 A four-variable method using a one-transistor–one-resistor (1T1R) cell
16.4.3 Other memristive V-R logic methods
16.4.4 Conclusion of the memristive V-R logic
16.5 Challenges and outlooks
Acknowledgments
References
17 Implementation of organic RRAM with ink-jet printer: from design to using in RFID-based application
17.1 Introduction
17.2 Design process
17.3 Fabrication process
17.4 A practical application example
17.5 Conclusion
References
18 Neuromorphic vision networks for face recognition
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
18.8.1 Edge detection and template formation
18.9 Experimental setup
18.10 Results and discussion
18.11 Future research directions
18.12 Conclusion
18.13 Key terms and definitions
References
19 Synaptic devices based on HfO2 memristors
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
19.5.1 Simulation schemes for the description of memristor physics
19.5.2 Kinetic Monte Carlo simulation approach
19.5.3 Kinetic Monte Carlo simulation of conductive-bridge RAMs
19.5.4 Valence change memories kinetic Monte Carlo simulation
19.6 Memristor compact modeling
19.6.1 VCM compact modeling
19.6.2 Compact modeling of CBRAMs
19.7 Memristor random telegraph noise
19.7.1 Numerical procedures to analyze random telegraph signals
19.7.1.1 Current versus time trace representation
19.7.1.2 Time lag plot (TLP) representation
19.7.1.3 Color code time lag plot (CCTLP)
19.7.1.4 Radius time lag plot (RTLP)
19.7.1.5 Weighted time lag plot (WTLP)
19.7.1.6 Locally weighted time lag plot (LWTLP)
19.7.1.7 Differential locally weighted time lag plot (DLWTLP)
19.8 Conclusion
Acknowledgments
References
20 Analog circuit integration of backpropagation learning in memristive HTM architecture
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
21 Multi-stable patterns coexisting in memristor synapse-coupled Hopfield neural network
21.1 Introduction
21.2 Memristor synapse-coupled HNN with three neurons
21.3 Bifurcation behaviors with multi-stability
21.3.1 Dynamics depended on the coupling strength k
21.3.2 Dynamics depended on the synaptic weights
21.3.2.1 2-dimensional bifurcation diagrams
21.3.2.2 Dynamics depended on the synaptic self-connection weight w11
21.3.2.3 Dynamics depended on the synaptic inter-connection weight w23
21.3.3 Dynamics depended on the initial conditions
21.3.4 Long-term transient chaotic behaviors
21.4 Circuit synthesis and PSIM simulation
21.5 Conclusion
Acknowledgment
References
22 Fuzzy memristive networks
22.1 Introduction
22.2 Requirement
22.2.1 Fuzzy calculus concepts
22.2.2 Fractional calculation concepts
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
23 Fuzzy integral sliding mode technique for synchronization of memristive neural networks
23.1 Introduction
23.2 Model description
23.3 Controller design
23.3.1 ISMC
23.3.2 FISMC
23.4 Numerical results
23.4.1 Memristive neural network without controller
23.4.2 Stabilization and comparison of the proposed method with the ISMC
23.4.3 Synchronization of uncertain memristive neural networks
23.5 Conclusions
References
24 Robust adaptive control of fractional-order memristive neural networks
24.1 Introduction
24.2 Model of the system and preliminary concepts
24.3 Controller design
24.4 Simulation results
24.4.1 Synchronization of fractional-order memristive neural networks
24.4.2 Comparison of the proposed adaptive controller method with PI control
24.5 Conclusion
References
25 Learning memristive spiking neurons and beyond
25.1 Introduction
25.2 Spike domain data processing and learning
25.3 Learning in memristive neuromorphic architectures
25.3.1 Spiking neural networks
25.3.2 Complex SNN typologies: long-short term memory and hierarchical temporal memory
25.3.3 Recent advances in memristor-based SNN architectures
25.4 Open problems and research directions
25.5 Conclusion
References
Index
Back Cover