Memristor-Based Chaotic Circuits

Muthuswamy, Bharathwaj; Kokate, Pracheta P.

doi:10.4103/0256-4602.57827

Cited by 297 publications

(172 citation statements)

References 16 publications

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“…The equations for the circuit are described by a set of ordinary differential equations: Figure 2. The four-element Chua's circuit: (a) with memristor; (b) with only the basic circuit elements, -k represents the effect of op -amp A 1 in (a) [3].…”

Section: The Memristor-based Chua's Circuitmentioning

confidence: 99%

“…100x10 −9 , and α 3 = 1 83.3x10 −3 as proposed in [3,4] in order to exhibit double scroll chaotic behavior. When the value of α 1 is decreased or the value of α 2 is increased, the equilibrium point, period-doubling sequences, and spiral and double scroll chaotic attractor behaviors can be observed.…”

Section: The Memristor-based Chua's Circuitmentioning

confidence: 99%

“…A memristor can lead to chaotic behavior when connected to a power source or when the Chua diode is replaced with a memristor [3,4,23,24]. In [24] several nonlinear oscillators utilized monotone increasing piecewise-linear memristors with passive characteristics, and in [2] a memristor was used instead of the Chua diode in Chua's circuit.…”

Section: Introductionmentioning

confidence: 99%

“…In [7] an autonomous circuit that uses only a linear passive inductor, a linear passive capacitor, and a memristor was proposed. The chaotic circuits like in [3,4,7] are used for applications of secure combination and image encryption [25]. Although there are many other variations of a memristor-based Chua's circuit in the literature used to obtain different chaotic behaviors, the current study aims to learn the behavior of the chaotic circuits in [3,4] due to the exhibition of chaotic behavior by a smaller number of components than the others [26].…”

Section: Introductionmentioning

confidence: 99%

“…First, the data in relation to the four variables obtained from the MATLAB model of the memristor-based chaotic circuit in [3,4] are spoilt by noise to obtain a high modeling accuracy. Without selecting the embedding dimension and the time delay, an input vector consisting of only the four state variables is used to train the ELMs in a simple and fast way.…”

Section: Introductionmentioning

confidence: 99%

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Behavior learning of a memristor-based chaotic circuit by extreme learning machines

Uçar¹,

Yavşan²

2016

Turk J Elec Eng & Comp Sci

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Abstract:As the behavior of a chaotic Chua's circuit is nonstationary and inherently noisy, it is regarded as one of the most challenging applications. One of the fundamental problems in the prediction of the behavior of a chaotic Chua's circuit is to model the circuit with high accuracy. The current paper presents a novel method based on multiple extreme learning machine (ELM) models to learn the chaotic behavior of the four elements canonical Chua's circuit containing a memristor instead of a nonlinear resistor only by using the state variables as the input. In the proposed method four ELM models are used to estimate the state variables of the circuit. ELMs are first trained by using the data spoilt by noise obtained from MATLAB models of a memristor and Chua's circuit. A multistep-ahead prediction is then carried out by the trained ELMs in the autonomous mode. All attractors of the circuit are finally reconstructed by the outputs of the models. The results of the four ELMs are compared to those of multiple linear regressors (MLRs) and support vector machines (SVMs) in terms of scatter plots, power spectral density, training time, prediction time, and some statistical error measures. Extensive numerical simulations results show that the proposed system exhibits a highly accurate multistep iterated prediction consisting of 1104 steps of the chaotic circuit. Consequently, the proposed model can be considered a promising and powerful tool for modeling and predicting the behavior of Chua's circuit with excellent performance, reducing training time, testing time, and practically realization probability.

show abstract

Section: The Memristor-based Chua's Circuitmentioning

confidence: 99%

Section: The Memristor-based Chua's Circuitmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Behavior learning of a memristor-based chaotic circuit by extreme learning machines

Uçar¹,

Yavşan²

2016

Turk J Elec Eng & Comp Sci

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Tunable Electroluminescence in Planar Graphene/SiO₂ Memristors

et al. 2013

Advanced Materials

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Electroluminescence and resistive switching are first realized simultaneously in graphene/SiO2 memristor devices. The electroluminescence peaks can be tuned between 550 nm and 770 nm reliably via setting the device to different resistance states by applying different voltages. The combination of resistive switching and electroluminescence may bring new functionalities for these memristor devices which are fully compatible with silicon-based electronics.

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2D Nanovaristors at Grain Boundaries Account for Memristive Switching in Polycrystalline BiFeO₃

Shen

Yin

Puzyrev

et al. 2015

Adv Elect Materials

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Memristive switching in polycrystalline materials is widely attributed to the formation and rupture of conducting filaments, believed to be mediated by oxygen‐vacancy redistribution. The underlying atomic‐scale processes are still unknown, however, which limits device modeling and design. Here, experimental data are combined with multiscale calculations to elucidate the entire atomic‐scale cycle in undoped polycrystalline BiFeO3. Conductive atomic force microscopy reveals that the grain boundaries behave like 2D nanovaristors, while on the return part of the cycle, the decreasing current is through the grains. Using density‐functional‐theory and Monte Carlo calculations, the atomic‐scale mechanism of the observed phenomena is deduced. Oxygen vacancies in nonequilibrium concentrations are initially distributed relatively uniformly, but they are swept into the grain boundaries by an increasing voltage. A critical voltage, the SET voltage, then eliminates the barrier for hopping conduction through vacancy energy levels in grain boundaries. On the return part of the cycle, the grain boundaries are again nonconductive, but the grains show nonzero conductivity by virtue of remote doping by oxygen vacancies. The RESET voltage amounts to a heat pulse that redistributes the vacancies. The realization that nanovaristors are at the heart of memristive switching in polycrystalline materials may open possibilities for novel devices and circuits.

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Memristor-Based Chaotic Circuits

Cited by 297 publications

References 16 publications

Behavior learning of a memristor-based chaotic circuit by extreme learning machines

Behavior learning of a memristor-based chaotic circuit by extreme learning machines

Tunable Electroluminescence in Planar Graphene/SiO₂ Memristors

2D Nanovaristors at Grain Boundaries Account for Memristive Switching in Polycrystalline BiFeO₃

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Memristor-Based Chaotic Circuits

Cited by 297 publications

References 16 publications

Behavior learning of a memristor-based chaotic circuit by extreme learning machines

Behavior learning of a memristor-based chaotic circuit by extreme learning machines

Tunable Electroluminescence in Planar Graphene/SiO2 Memristors

2D Nanovaristors at Grain Boundaries Account for Memristive Switching in Polycrystalline BiFeO3

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Product

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Tunable Electroluminescence in Planar Graphene/SiO₂ Memristors

2D Nanovaristors at Grain Boundaries Account for Memristive Switching in Polycrystalline BiFeO₃