Memristor-based Neural Networks Research Articles

Quantized dynamic event-triggered control for fixed/preset-time bipartite synchronization of memristor-based discontinuous multi-layer signed networks

This study addresses the fixed/preset-time bipartite synchronization (FTBS/PTBS) of memristor-based discontinuous multi-layer signed networks via quantized dynamic event-triggered control (ETC). Firstly, a memristor-based neural network model incorporating multi-layer coupling structures, signed networks and discontinuous activations is built. Subsequently, by designing two quantized dynamic ETC strategies without the linear feedback term, the FTBS and PTBS of the considered networks are discussed, and it is proved that Zeno behavior can be ruled out. In contrast to the previous static ETC mechanism, the introduced internal dynamic variable can significantly decrease the triggering times and further save communication resources. Lastly, three numerical examples are offered to verify the correctness and practicability of the theoretical analysis.

Dynamics analysis, synchronization and FPGA implementation of multiscroll Hopfield neural networks with non-polynomial memristor

The number of attractors in a memristor-based multiscroll Hopfield Neural Network (HNN) is typically coupled with the number of polynomials, which leads to a coupling between the computational complexity and resource utilization in circuit implementation. To decouple this relationship, we propose a non-polynomial memristor that satisfies the Lipschitz condition. Regardless of whether it is used to simulate synaptic behavior, simulate the impact of electromagnetic radiation, or a combination of both scenarios, it can conveniently control the generation of single-direction or multiple-direction multiscroll attractors without adding or reducing any terms. By constructing Lyapunov functions, the sufficient condition for these multiscroll memristor HNNs to be bounded is obtained. After improving the feasibility of linear matrix inequalities, a strongly adaptive observer is proposed. After uniting an adaptive sliding mode control method, we propose a new adaptive synchronization scheme to simulate neural network synchronization. Finally, the digital circuit implementation and functional verification of these memristor-based multiscroll HNNs are completed using a field-programmable gate array (FPGA). Based on this, an image encryption circuit is designed so that the FPGA can directly encrypt images and transmit them to the IO device.

Synchronization analysis of delayed quaternion-valued memristor-based neural networks by a direct analytical approach

<abstract><p>This issue discusses the asymptotic synchronization and the exponential synchronization for memristor-based quaternion-valued neural networks under the time-varying delays. Some criteria for synchronization of the memristor-based quaternion-valued neural networks are given by exploiting the set-valued theory, the differential inclusion theory, some analytic techniques, as well as constructing novel controllers, It is worth noting that the synchronization problem about the memristor-based quaternion-valued neural networks were studied by the direct analysis method in this paper. Finally, the main theoretical results were verified by numerical simulations.</p></abstract>

Exponential Synchronization of Memristor-Based Competitive Neural Networks With Reaction-Diffusions and Infinite Distributed Delays.

Taking into account the infinite distributed delays and reaction-diffusions, this article investigates the global exponential synchronization problem of a class of memristor-based competitive neural networks (MCNNs) with different time scales. Based on the Lyapunov-Krasovskii functional and inequality approach, an adaptive control approach is proposed to ensure the exponential synchronization of the addressed drive-response networks. The closed-loop system is a discontinuous and delayed partial differential system in a cascade form, involving the spatial diffusion, the infinite distributed delays, the parametric adaptive law, the state-dependent switching parameters, and the variable structure controllers. By combining the theories of nonsmooth analysis, partial differential equation (PDE) and adaptive control, we present a new analytical method for rigorously deriving the synchronization of the states of the complex system. The derived m-norm (m ≥ 2)-based synchronization criteria are easily verified and the theoretical results are easily extended to memristor-based neural networks (NNs) without different time scales and reaction-diffusions. Finally, numerical simulations are presented to verify the effectiveness of the theoretical results.

Reliable and Energy-Efficient Diabetic Retinopathy Screening Using Memristor-Based Neural Networks

Reliable and Energy-Efficient Diabetic Retinopathy Screening Using Memristor-Based Neural Networks

Relaxed Stability Criteria for Delayed Memristor-Based Neural Network Systems via a Novel Matrix-Separation Legendre Inequality.

This article studies the issue of stability in memristor-based neural network (MNN) systems with time-varying delays. First, a novel matrix-separation Legendre inequality is proposed to achieve a tight hierarchical bound on augmented-type integral terms. To derive implementable inequality conditions, several delay-dependent matrices are introduced to eliminate the reciprocal terms associated with time-varying delay. Furthermore, a new Lyapunov-Krasovskii (L-K) functional is proposed by incorporating augmented-type double integrals and delay-product terms. A series of free-weighting matrices are introduced into the L-K functional, leveraging the zero-sum equations and the S-procedure pertaining to both the delay and its derivative. Based on the proposed matrix-separation Legendre inequality and L-K functional, the derived stability conditions exhibit reduced conservatism, as validated by three numerical cases and simulation results.

Synchronization of inertial complex-valued memristor-based neural networks with time-varying delays.

The synchronization of inertial complex-valued memristor-based neural networks (ICVMNNs) with time-varying delays was explored in the paper with the non-separation and non-reduced approach. Sufficient conditions required for the exponential synchronization of the ICVMNNs were identified with the construction of comprehensive Lyapunov functions and the design of a novel control scheme. The adaptive synchronization was also investigated based on the derived results, which is easier to implement in practice. What's more, a numerical example that verifies the obtained results was presented.

Memristor-Based Neural Network Circuit of Full-Function Pavlov Associative Memory With Unconditioned Response Mechanisms

Memristor-Based Neural Network Circuit of Full-Function Pavlov Associative Memory With Unconditioned Response Mechanisms

Memristor-Based Neural Network Circuit of Associative Memory With Overshadowing and Emotion Congruent Effect.

Most memristor-based neural network circuits consider only a single pattern of overshadowing or emotion, but the relationship between overshadowing and emotion is ignored. In this article, a memristor-based neural network circuit of associative memory with overshadowing and emotion congruent effect is designed, and overshadowing under multiple emotions is taken into account. The designed circuit mainly consists of an emotion module, a memory module, an inhibition module, and a feedback module. The generation and recovery of different emotions are realized by the emotion module. The functions of overshadowing under different emotions and recovery from overshadowing are achieved by the inhibition module and the memory module. Finally, the blocking caused by long-term overshadowing is implemented by the feedback module. The proposed circuit can be applied to bionic emotional robots and offers some references for brain-like systems.

A Memristor-Based Neural Network Circuit With Latent Inhibition and Transient Forgetting Effects and Application in Industrial Intelligent Grasping

A Memristor-Based Neural Network Circuit With Latent Inhibition and Transient Forgetting Effects and Application in Industrial Intelligent Grasping

Circuit Implementation and Quasi-Stabilization of Delayed Inertial Memristor-Based Neural Networks.

In this brief, we consider the stability of inertial memristor-based neural networks with time-varying delays. First, delayed inertial memristor-based neural networks are modeled as continuous systems in the flux-current-voltage-time domain via the mathematical model of Hewlett-Packard (HP) memristor. Then, they are reduced to delayed inertial neural networks with interval parameters uncertainties. Quasi-equilibrium points and quasi-stability are proposed. Quasi-stability criteria of delayed inertial memristor-based neural networks are obtained by matrix measure method, the Halanay inequality, and uncertainty technologies. In the end, a numerical example is provided to show the validity of our results.

Scroll-growing/controlling chaotic attractors in cyclic Hopfield neural networks via memristive bridging

In this paper, a memristor-based neural network is proposed, which is implemented by two tri-neuron resistive-cyclic Hopfield neural networks (RC-HNNs) via memristive bridging. The memristor-bridged network has a line equilibrium set composed of infinitely many index-2 saddle-foci, but it can produce multi-scroll chaotic attractors contrary to Shil’nikov’s criterion. Complex bifurcation behaviors, scroll-growing chaotic attractors over time, and homogeneous coexisting attractors are revealed by numerical methods. Further, a scroll-control scheme is designed and scroll-controlling chaotic attractors are demonstrated numerically. The results show that the memristor-bridged network can not only generate scroll-growing chaotic attractors over time, but also produce scroll-controlling chaotic attractors by limiting the dynamic range of the internal state of the bridging memristor. Finally, an analog electronic circuit is designed for the memristor-bridged network, and PSIM circuit simulations are used to verify the numerical simulations.

Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks

Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive uncertainty assessment. However, because of their probabilistic nature, they are computationally intensive. An innovative solution utilizes memristors’ inherent probabilistic nature to implement Bayesian neural networks. However, when using memristors, statistical effects follow the laws of device physics, whereas in Bayesian neural networks, those effects can take arbitrary shapes. This work overcome this difficulty by adopting a variational inference training augmented by a “technological loss”, incorporating memristor physics. This technique enabled programming a Bayesian neural network on 75 crossbar arrays of 1,024 memristors, incorporating CMOS periphery for in-memory computing. The experimental neural network classified heartbeats with high accuracy, and estimated the certainty of its predictions. The results reveal orders-of-magnitude improvement in inference energy efficiency compared to a microcontroller or an embedded graphics processing unit performing the same task.

Memristor-Based Neural Network Circuit of Duple-Reward and Duple-Punishment Operant Conditioning With Time Delay

Memristor-Based Neural Network Circuit of Duple-Reward and Duple-Punishment Operant Conditioning With Time Delay

Stabilization analysis of incommensurate fractional-order memristor-based neural networks via delay-dependent distributed controller

This paper studies the stabilization analysis problem of a class of incommensurate fractional-order memristor-based neural networks with multiple time-varying delays (IFOMNNs-MTDs). In previously published studies of IFOMNNs-MTDs, the controller is usually designed as a general delay-independent feedback controller. Due to the simple form of the feedback controller, less system information is used and less adjustable parameters are considered, which limits the flexibility and control effect of controller. Specially, the use of delay information is insufficient in the existing results, which will make the controller insensitive to the influence of delay factors and affect the control performance. Thereby, the accurate analysis of the dynamic characteristics of IFOMNNs-MTDs by designing appropriate controller needs further study. This paper aims to propose a new delay-dependent controller to improve the stabilization analysis of IFOMNNs-MTDs. Firstly, a delay-dependent distributed controller with distributed control gain and time-delay state summation term is proposed, which accords with the transmission law of activation function weights and is helpful to consider the historical information (i.e., time delay factors) of system. Thereby, the flexibility and control effect of controller are improved by adding control parameters and improving the use of system information. Secondly, a new less-conservatism stabilization criterion of IFOMNNs-MTDs is established by using the designed controller. Moreover, the controller gain can be searched in a large range by using the established criterion. Finally, a numerical simulation is provided to verify the validity of the obtained results.

Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise

Research on discrete memristor-based neural networks has received much attention. However, current research mainly focuses on memristor–based discrete homogeneous neuron networks, while memristor-coupled discrete heterogeneous neuron networks are rarely reported. In this study, a new four-stable discrete locally active memristor is proposed and its nonvolatile and locally active properties are verified by its power-off plot and DC V–I diagram. Based on two-dimensional (2D) discrete Izhikevich neuron and 2D discrete Chialvo neuron, a heterogeneous discrete neuron network is constructed by using the proposed discrete memristor as a coupling synapse connecting the two heterogeneous neurons. Considering the coupling strength as the control parameter, chaotic firing, periodic firing, and hyperchaotic firing patterns are revealed. In particular, multiple coexisting firing patterns are observed, which are induced by different initial values of the memristor. Phase synchronization between the two heterogeneous neurons is discussed and it is found that they can achieve perfect synchronous at large coupling strength. Furthermore, the effect of Gaussian white noise on synchronization behaviors is also explored. We demonstrate that the presence of noise not only leads to the transition of firing patterns, but also achieves the phase synchronization between two heterogeneous neurons under low coupling strength.

Finite/Fixed-Time Synchronization of Memristor-Based Fuzzy Neural Networks with Markov Jumping Parameters Under Unified Control Schemes

Finite/Fixed-Time Synchronization of Memristor-Based Fuzzy Neural Networks with Markov Jumping Parameters Under Unified Control Schemes

Memristor-Based Neural Network Circuit of Emotional Habituation With Contextual Dependency

Most memristor-based neural networks only consider habituation under repeated stimuli, but the emotional habituation under repeated emotional stimuli is ignored. In this paper, a memristor-based neural network circuit that can realize emotional habituation with contextual dependency is proposed. The designed circuit consists of habituation module, emotion control module, context control module and generalization module. When different emotional stimuli are applied, the emotion control module produces different feedback voltages and has an impact on the formation of habituation. In addition, the influence of contextual information on habituation is considered and the contextual dependency of habituation is achieved. Finally, the generalization of habituation is achieved through the generalization module. The simulation results in PSPICE indicate that the proposed circuit can realize emotional habituation with contextual dependency. The neural network circuit of emotional habituation with contextual dependency provides some references for brain-like intelligence.

Quasi-projective synchronization of memristor-based complex valued recurrent neural network with time-varying delay and mismatched parameters

This article focuses on the quasi-projective synchronization for a class of non-identical memristor-based complex-valued recurrent neural networks (MCVRNNs) with time-varying delays and parameters’ mismatched. Here, the quasi-projective synchronization is presented in two ways based on the Halanay inequality and some useful techniques. Some novel sufficient conditions are firstly derived for quasi-projective synchronization criteria of non-identical memristor-based CVRNNs with time-varying delays and parameters’ mismatched by using the matrix measure method. And then, by using the Lyapunov-Krasovskii functional, another sufficient stabilization condition for quasi-projective synchronization of the concerned model is presented. Finally, two numerical simulation results are given to demonstrate the usefulness and persistence of the proposed methods under some conditions.

Research on the construction method of neural network model based on memristors

Abstract With the continuous development of neural networks, the hardware requirements are getting higher and higher, and the emergence of memristors is expected to optimize this challenge. In this paper, by studying the inter-mapping relationship between memristor arrays and neural networks, a memristor-based convolutional neural network circuit module is designed, a binarization method is used to quantize the neural network, an acceleration module is constructed based on multiple computational arrays, and an additive tree is used to accumulate the intermediate results of the output values of multiple computational units. Simulation experiments were conducted with the help of simulation software to compare and analyze the memristor-based CNN with other neural networks. Compared with LSTM, RNN, and ELM, the accuracy of the memristor-based CNN is 4.17%, 7.48%, and 2.01% higher compared to other neural networks, respectively. In the performance analysis, the recognition rate of the memristor CNN is almost unaffected by the programming error and still achieves a recognition rate close to 98% in the best case. This study provides a new idea for implementing and applying convolutional neural networks in memristor arrays, which is expected to promote the further development of memristor neuromorphic computing.