Memristive System Research Articles

Dynamical analysis and preassigned-time intermittent control of memristive chaotic system via T-S fuzzy method.

In this paper, we propose a novel fourth-order memristive chaotic system (MCS), in which both its dynamical behaviors and the preassigned-time stabilization problem are analyzed. First, the dynamical behaviors of the proposed MCS are studied in detail, such as the infinite unstable equilibrium points, the chaotic attractor, the Lyapunov exponents, the Kaplan-Yorke dimension, and the bifurcation. Then, the T-S fuzzy method is employed to characterize the MCS, and a simpler model is built to deal with the nonlinearity caused by the memristor in the MCS. In addition, two intermittent controllers are proposed to guarantee the preassigned-time stability and the settling time, which can be set freely, independent of system parameters and initial state. Finally, numerical simulations provide solid confirmation for the validity of these theoretical results.

Enhancing colour image encryption through parameters optimization of memristive hyperchaotic system with CPSO algorithm and LSAIM

ABSTRACT Particle Swarm Optimization (PSO) is a simple and effective algorithm for optimization. However, it often converges too early and depends heavily on proper parameter settings for good results, especially in complex or high-dimensional search spaces. Therefore, this algorithm is inappropriate for parameter optimization of hyperchaotic systems and may not provide sufficient robustness and security of colour images. This study presents a new colour image encryption based on optimizing a four-dimensional Memristor-based hyperchaotic system, in which the fourteen parameters of the hyperchaotic system are optimized by the Chaotic Particle Swarm Optimization (CPSO) method, in combination with DNA code and a new logistic sine adjusted integrated map (LSAIM). Simulation results have shown that the algorithm achieved a key space of up to 21116, providing sufficient protection against brute-force attacks, with entropy values close to the ideal (7.9994), NPCR of 99.6178%, and UACI of 33.965%, indicating robust security.

Analysis and circuit implementation of fractional-order memristive hyperchaotic system with enhanced memory

Abstract In this paper, based on the memory characteristics of fractional calculus, a new fractional-order memristor is proposed. Fractional-order memristor is a more accurate description of memristor, which has richer dynamic behavior and better memory performance. Which has a stronger memorizability compared to other fractional-order memristor by analyzing the pinched hysteresis loop area. Based on the above fractional-order memristor, a fractional-order memristive hyperchaotic circuit is designed, such system is analyzed by using the Lyapunov Exponents and the bifurcation diagrams. With the change of system parameters, the phase trajectory of the system expands and narrows, and the amplitude of the chaotic attractor also changes. In addition, double chaotic attractors and coexisting attractors are found under different parameters and initial values. Finally, the fractional order memristor and the fractional order memristor hyperchaos circuit are realized by analog circuit in Multisim.

A new color image encryption scheme based on DNA sequence operations and novel hyper-chaotic system

Abstract Aiming at the problems of weak key sensitivity and small key space of
image encryption algorithm,in this paper,we propose an image encryption algorithm
based on four-dimensional memristive hyperchaotic system and DNA operation. The
algorithm first calculates the initial value of the image information by Secure Hash
Algorithm 512(SHA-512) hash algorithm, substitutes it into the four-dimensional
memory-damped hyperchaotic system to generate a sequence, and then obtains the key
map by Logistic mapping and Sine mapping.The preprocessed image matrix is obtained
by dot-multiplication operation of the plaintext image with the square matrix generated
by Sigmoid algorithm after Arnold permutation.Then DNA encoding,DNA row-by-
row operation and DNA decoding are performed on the key map and preprocessed
image, and at the end, the encrypted image is obtained after rotating the first round
encrypted image and the key map by 90° for the second encryption.Experiments such as
robustness analysis,anti-differential attack,and adjacent pixel correlation analysis show
that the algorithm has a large key space,strong key sensitivity and the encrypted image
has a small adjacent pixel correlation.It is shown that the algorithm can effectively
protect the communication encryption of digital images.

Extremely multi-stable grid-scroll memristive chaotic system with omni-directional extended attractors and application of weak signal detection

The extreme multi-stability and omni-directional expansion of the attractors in chaotic systems play an indispensable role in the chaos-based engineering applications. In this study, a novel extremely multi-stable grid-scroll memristive chaotic system with omni-directional extended attractors is proposed, and applies it to weak signal detection. Firstly, a flux-controlled non-volatile memristor with a typical 8-shaped pinched hysteresis loop, and three piecewise linear functions that can promote the position offset of the attractor are designed. On this basis, we construct a novel grid-scroll memristive chaotic system with infinite equilibrium points, which can generate n × n × n-scroll attractors. The piecewise linear functions lead to the infinite expansion of the chaotic attractor in the x, y, and z directions, which also indirectly causes the position offset in the w direction. This phenomenon has not been discovered in existing researches. The numerical analysis demonstrates that the memristive chaotic system has infinite coexisting attractors, and a built-in parameter that can achieve global amplitude control of the attractor has also been verified. The omni-directional expansion, coexistence and global amplitude control of the attractor illustrates the extreme multi-stability of the designed system. The analog circuit of the memristive chaotic system has been implemented to verify its feasibility and practicability. Finally, a weak signal detection system based on the grid-scroll memristive chaotic system is constructed. This system not only has excellent robustness to various noise, but also is more sensitive to weak periodic signals. The detection signal-to-noise ratio (SNR) threshold reaches -63 dB. The applicability of this method in practical engineering is verified through the detection of ship radiation signals submerged in ocean background noise.

Multilevel Optical Storage, Dynamic Light Modulation, and Polarization Control in Filamented Memristor System.

The electrochemical metallization (ECM) mechanism is emerging as a promising approach for the development of optical memristors-nonvolatile memory systems proposed for use as artificial synapses in neuromorphic computing applications. ECM memristors offer exceptional operating dynamics and power efficiency compared to other systems, but challenges with reproducible cycle-to-cycle state switching and the absence of advanced optical functionalities hinder their integration into photonic systems. In this work, an ECM free-standing memristor structure is proposed, which simultaneously offers wavelength-dependent multilevel nonvolatile optical storage, volatile light modulation, and dynamic polarization control. It is demonstrated that in the presence of a resonance, the optical readout provides noise-free, robust, and significantly more accurate information about the memristor's state than electrical measurement. The use of light allows to gain insight into the intermediate electrical levels of the device as it transitions between high and low resistance states and to recover the complete record of applied voltages even when stochastic filament ruptures occur. Finally, the investigations show that spectroscopic ellipsometry provides real-time information on the dynamics of cation movement and the corresponding permittivity changes at the interfaces between the switching layer and the electrodes, thus becoming a complementary characterization method for ECM memristors alongside state-of-the-art techniques.

MXene/WO3 Sensor Array with Improved SNN Algorithm for Accurate Identification of Toxic Gases.

Gas sensing is pivotal in critical areas such as industrial production and food safety. This study explores the gas classification capabilities of MXene-based gas sensors. Pure V2CTx MXene and an MXene/WO3 nanocomposite were synthesized, and MXene-based gas sensors were integrated into a 2 × 2 rudimentary electronic nose array. The tests on gas sensitivity revealed that the inclusion of WO3 nanoparticles (NPs) boosted the sensor's response to 10 ppm of NO2 from 2.82 to 3.45 at room temperature. Moreover, the sensor showcased a rapid response/recovery duration of 74.5/149.0 s, excellent environmental stability, and long-term reliable sensing performance. Furthermore, we have improved the method of accurately identifying four toxic gases detected by an MXene-based sensor array using a spiking neural network (SNN) based on the memristive system. Also, the performance of this identification method revealed that the method achieved 95.83% accuracy in the identification of the four gases. Notably, the improved SNN demonstrated approximately 5% higher accuracy than the other gas recognition algorithm. These results highlight the potential of SNN as a powerful tool to accurately and reliably identify toxic gases based on the gas sensor array.

Complex dynamics and encryption application of memristive chaotic system with various amplitude modulations

Complex dynamics and encryption application of memristive chaotic system with various amplitude modulations

Coexistence of hidden attractors in memristive chaotic system

In this paper, a charge controlled memristor model is introduced into the Sprott-A system equation to construct a new memristor chaotic system and the calculation of this new system satisfies the characteristics of no equilibrium points. The periodic function is added to the new constructed memristor chaotic system, and the coexistence of attractors in memristor chaotic system without equilibrium points is obtained by adjusting the control parameters. Through different analytical methods to explore the characteristics of the new system. The dynamic behaviors of the system before and after the periodic transformation are compared and analyzed. In the end, DSP simulation is used to verify the feasibility of the theoretical model. The coexistence of attractors in memristor chaotic systems can improve the flexibility and security of chaotic encryption systems. Further research on this kind of phenomena can meet the needs of higher encryption.

Design and FPGA realization of memristive multibutterfly chaotic system featuring coexisting attractors and complex multistability behavior

Design and FPGA realization of memristive multibutterfly chaotic system featuring coexisting attractors and complex multistability behavior

Exponential Synchronization Control of Reaction-Diffusion Fuzzy Memristive Neural Networks: Hardy-Poincarè Inequality.

This article is devoted to solving the exponential synchronization problem of a new type of fuzzy memristive neural network with reaction-diffusion terms. By introducing adaptive laws, two controllers are designed. After combining the inequality technique with the Lyapunov function approach, some easily verified sufficient conditions are established to ensure the exponential synchronization of the reaction-diffusion fuzzy memristive system under the proposed adaptive scheme. In addition, by using the Hardy-Poincarè inequality, the diffusion terms are estimated associated with the information of the reaction-diffusion coefficients and the regional feature, which improves some existing conclusions. Finally, an illustrative example is presented to demonstrate the validity of the theoretical results.

Interior Crisis Route to Extreme Events in a Memristor-Based 3D Jerk System

In dynamical systems, events that deviate significantly from usual or expected behavior are referred to as extreme events. This paper investigates the mechanism of extreme event generation in a 3D jerk system based on a generalized memristive device. In addition, regions of coexisting parallel bifurcation branches are explored as a way of investigating the multistability of the memristive system. The system is examined using bifurcation diagrams, Lyapunov exponents, time series, probability density functions of events, and inter-event intervals. It is found that extreme events occur via a period-doubling route and are due to an interior crisis that manifests itself as a sudden shift from low-amplitude to high-amplitude oscillations. Multistability is also identified when both control parameters and initial values are modified. Finally, an analog circuit based on the memristive jerk system is designed and experimentally realized. To our knowledge, this is the first time that extreme events have been reported in a memristive jerk system in particular and in jerk systems in general.

An image encryption scheme based on an improved memristive neuron chaotic system

With fast-developing Internet and communication, the security transmission of image in network has become a research highlight. So far, researchers have designed a lot of image encryption methods based on chaotic models, some of them are not secure enough. To enhance security of image transmission on the network, in this paper, an image encryption method is developed from a 3D memeristive FitzHugh-Nagumo (FHN) neuron. First, a 3D memeristive FHN model is obtained by connecting a memeristor into a 2D FHN model, and dynamics for 3D FHN model are estimated by applying phase diagrams, bifurcation and Lyapunov exponent. Then, an image encryption algorithm is proposed by using this 3D model. Finally, security of encryption algorithm is estimated. Simulation results confirm the effectiveness of encryption scheme.

Short‐Term and Long‐Term Memory Functionality of a Brain‐Like Device Built from Nanoparticle Atomic Switch Networks

AbstractThe synaptic plasticity of the Ag‐Ag2S nanoparticle‐based volatile memristor system is demonstrated. The nanoparticles self‐assemble into a network with over 103 interconnected atomic switch interfaces. Short‐term plasticity is identified by spontaneous conductance relaxation, attributed to the memristor's volatility. The conductance of the network is enhanced when a subsequent stimulus pulse arrives shortly after the previous one, analogous to the paired‐pulse facilitation in biological synapses. Furthermore, repeated pulse stimulation is used to achieve the transition from short‐term plasticity to long‐term potentiation, a process related to learning and memory formation. Remarkably, the result reveals that the lifetime of long‐term potentiation for 100‐pulse stimulation is 40 min, indicating that the device can forget newly acquired information after prolonged storage, akin to human memories. The findings provide insight into the the learning and memory abilities of atomic switch network memristors, facilitating the development of hardware‐implemented artificial neural networks.

A new four-valued memristive hyperchaotic system with hidden attractors and multistability

A four-valued memristor is designed based on the memristor model and introduced into a three-dimensional chaotic system to construct a four-dimensional four-valued memristive chaotic system, and its dynamics are investigated and discussed. The properties of the system such as phase diagrams, Poincaré map, dissipation, equilibrium points stability, Lyapunov exponents, Lyapunov dimensions, dynamical complexity, offset boosting, inversion properties, and attractor coexistence are investigated. The system is found to have infinitely many equilibrium points with hidden attractors, and has higher dimensionality, and complexity than the original system. As the parameters change, the new system transitions from a quasi-periodic state to a chaotic state to a hyperchaotic state and finally back to a quasi-periodic state through a reverse period-doubling bifurcation. The complexity of the system is verified using the C0 algorithm and the spectral entropy algorithm. The offset boosting control of the system can be realized by adding parameters to the system. The system also has phase reversal characteristics of a single parameter control system attractor, and there are multiple types of attractors coexisting with changes in the system parameters at different initial values. In addition, when the parameters of the system are fixed, the existence of exterme multistability based on the initial value of w0 the memristor is found. Finally, the feasibility of the design and the correctness of the analysis are verified by simulated circuits. Therefore, the system is highly complex and can be applied in the fields of image encryption and confidential communication.

A novel memristive chaotic jerk circuit and its microcontroller-based sliding mode control

Due to the experimental realization of memristor circuit elements, research on memristors and memristor-based circuits has surged. Because of their nonvolatile and nonlinear behavior, memristors can be easily applied to chaotic circuits. This study introduces a novel memristive 3D chaotic jerk system, comprising only seven terms, along with its electronic model and microcontroller-based control. The flux-controlled memristor-based jerk system exhibits complex dynamics, which were analyzed through various properties such as phase portraits, the Jacobian matrix, equilibria, eigenvalues, Lyapunov spectra, bifurcation diagrams, and transient chaos behavior. Three controllers, namely, nonlinear feedback, classical sliding mode, and integral sliding mode were designed to control the chaotic jerk oscillator. Lyapunov functions were used to synthesize the nonlinear feedback controller and ensure system stability with the sliding mode technique. Numerical tests under various performance criteria and disturbance conditions showed that the sliding mode controller outperforms the nonlinear feedback controller due to its single-state control structure. The chaotic jerk oscillator hardware circuit was designed and implemented, operating easily with initial conditions set to zero and low DC supply voltages, with all output voltages within ±6V. Both theoretical and simulation results demonstrate the system’s complexity and applicability, with experimental results aligning well with simulations. Consequently, effective microcontroller-based control was achieved using a single-state controller.

A novel family of asymmetric generalized passive voltage‐controlled memristive system

AbstractResearch on the physical memristive system is vital for realizing widespread applications of the memristor in practical engineering. In this article, a family of the asymmetric generalized passive voltage‐controlled memristive system (AGPVCMS) is proposed; also the mathematical model of the AGPVCMS is established and its remarkable memristive characteristics are analysed. Especially, the AGPVCMS only consists of the diode bridge and LR filter, however it can enhance its asymmetry as well as display various asymmetrical pinched hysteresis loops in the voltage–current plane, via expediently changing the number of the diodes on different bridge arms. In addition, the effectiveness of the AGPVCMS is verified by the consistency between theoretical and experimental results.

Resistive Switching Acceleration Induced by Thermal Confinement

AbstractEnhancing the switching speed of oxide‐based memristive devices at a low voltage level is crucial for their use as non‐volatile memory and their integration into emerging computing paradigms such as neuromorphic computing. Efforts to accelerate the switching speed often result in an energy trade‐off, leading to an increase in the minimum working voltage. In this study, an innovative solution is presented: the introduction of a low thermal conductivity layer placed within the active electrode, which impedes the dissipation of heat generated during the switching process. The result is a notable acceleration in the switching speed of the memristive model system SrTiO3 by a remarkable factor of 103, while preserving the integrity of the switching layer and the interfaces with the electrodes, rendering it adaptable to various filamentary memristive systems. The incorporation of HfO2 or TaOx as heat‐blocking layers not only streamlines the fabrication process but also ensures compatibility with complementary metal‐oxide‐semiconductor technology.

(Invited) Engineering the Switching Kinetics of Valence Change-Based Memristive Devices for Neuromorphic Computing

Memristive devices based on the valence mechanism are highly interesting candidates for the use as hardware representation of synapses in neuromorphic computing. The memristive model system SrTiO3 exhibit gradual switching and can be tuned between short-term and long-term plasticity. We will discuss the impact of the switching kinetics on the gradual switching mode and provide general guidelines for the design of gradual switching systems. Moreover, we present an approach to accelerate the switching kinetics of SrTiO3 by up to 103 times, or reduce the operating voltage by ≈ 30% to maintain the switching speed.Our approach is to introduce a low thermal conductivity layer inside the active electrode of the active electrode of the devices, which blocks the heat dissipation caused by Joule heating during switching. Our method leaves the switching layer and its interfaces with the electrodes intact, while the use of HfO2 and TaOx as the heat blocking layers ensures ease of fabrication and CMOS compatibility. We will demonstrate that this approach is transferable to other more common material systems.

Automatic Learning Rate Adaption for Memristive Deep Learning Systems.

As a possible device to further enhance the performance of the hybrid complementary metal oxide semiconductor (CMOS) technology in the hardware, the memristor has attracted widespread attention in implementing efficient and compact deep learning (DL) systems. In this study, an automatic learning rate tuning method for memristive DL systems is presented. Memristive devices are utilized to adjust the adaptive learning rate in deep neural networks (DNNs). The speed of the learning rate adaptation process is fast at first and then becomes slow, which consist of the memristance or conductance adjustment process of the memristors. As a result, no manual tuning of learning rates is required in the adaptive back propagation (BP) algorithm. While cycle-to-cycle and device-to-device variations could be a significant issue in memristive DL systems, the proposed method appears robust to noisy gradients, various architectures, and different datasets. Moreover, fuzzy control methods for adaptive learning are presented for pattern recognition, such that the over-fitting issue can be well addressed. To our best knowledge, this is the first memristive DL system using an adaptive learning rate for image recognition. Another highlight of the presented memristive adaptive DL system is that quantized neural network architecture is utilized, and there is therefore a significant increase in the training efficiency, without the loss of testing accuracy.