Exponential Synchronization of Memristive Chaotic Recurrent Neural Networks Via Alternate Output Feedback Control

This paper considers the global exponential synchronization problem of two memristive chaotic recurrent neural networks with time‐varying delays using periodically alternate output feedback control. First, the periodically alternate output feedback control rule is designed for the global exponential synchronization of two memristive chaotic recurrent neural networks. Then, according to the Lyapunov stability theory, we construct an appropriate Lyapunov‐Krasovskii functional to derive several new sufficient conditions guaranteeing exponential synchronization of two memristive chaotic recurrent neural networks under periodically alternate output feedback control. Compared with existing results on synchronization conditions on the basis of linear matrix inequalities of memristive chaotic recurrent neural networks, the derived results complement, extend earlier related results, and are also easy to validate in this paper. An illustrative example is provided to illustrate the effectiveness of the synchronization criteria.     

The Intermittent Control Synchronization of Complex-Valued Memristive Recurrent Neural Networks with Time-Delays

In this paper, the intermittent control synchronization of complex-valued memristive recurrent neural networks with time-delays is investigated. As a generalization on the real-valued memristive recurrent neural networks, complex-valued memristive recurrent neural networks own more complicated properties. In complex-valued domain, bounded and analytic complex-valued activation functions do not exist. Some assumptions about activation functions in real-valued domain cannot be applied directly to complex-valued fields. By appropriate transformation, complex-valued memristive recurrent neural networks can be divided into real parts and imaginary parts, which can avoid discussing the bounded and analytic. In the framework of differential inclusion theory and Lyapunov method, sufficient criteria of intermittent control synchronization are established. Finally, a simulation is given to verify the validity and feasibility of the sufficient conditions.

     

Fixed-time Synchronization of Memristive Cohen-Grossberg Neural Networks with Impulsive Effects

This paper investigates the fixed-time synchronization of memristive Cohen-Grossberg neural networks with impulsive effects. Through a nonlinear transformation and Fillipov discontinuous theory, we obtain an equivalent system from the original memristive Cohen-Grossberg neural networks. By constructing a discontinuous Lyapunov function and utilizing comparison principle, a sufficient condition is achieved to guarantee the fixed-time synchronization of drive-response system with impulsive effects. Moreover, for the purpose of reducing the cost of control, an adaptive control strategy is considered. Finally, corresponding numerical simulations are carried out to show the effectiveness of the analytic results.     

一类时滞混沌忆阻器神经网络的延迟反同步控制

研究了一类时滞混沌忆阻器神经网络的延迟反同步控制问题．通过构造李亚普诺夫函数及采用微分包含理论和Halanay不等式的研究方法,设计了一个线性反馈控制器,并恰当选择控制器增益实现了一类混沌忆阻器神经网络驱动系统与响应系统之间的延迟反同步,所设计的控制器简单并易于实现．最后,仿真例子验证了所设计的控制器的有效性．     

惯性忆阻神经网络固定时间抗干扰聚类同步控制

本文研究了具有外部干扰的惯性忆阻神经网络固定时间聚类同步控制问题. 通过设计一类经济型的固定 时间控制器, 保证了耦合网络系统在固定时间内以较少的能量消耗实现聚类同步. 为提高网络的普适性, 文中神经 网络的激活函数为非连续函数. 基于Filippov解理论以及固定时间控制理论, 获得了耦合网络的同步性判据, 给出了 收敛时间的具体上界, 验证了网络抗干扰能力. 最后, 通过数值仿真说明了理论结果的有效性.     

局部耦合的双层忆阻神经元网络的嵌合态和同步

嵌合态被发现存在于神经系统并且可能在神经元节律、大脑的睡眠和记忆等诸多神经过程中发挥重要作用.本文考虑神经元交互中的电磁感应现象,建立了以Hindmarsh Rose神经元为节点的局部耦合的双层忆阻神经元网络,研究其嵌合态时空动力学模式及产生机理.结果发现,改变层内、层间突触耦合强度会使网络产生移动和不完美移动嵌合态等多种类型的嵌合模式,其中不完美移动嵌合态中不相干的区域会扩展到网络的相干域.特别地,在特定耦合强度下,存在一种新的嵌合态活动模式,即一部分神经元处于嵌合态,另一部分神经元处于移动嵌合态.考虑神经元突触的忆阻特性,发现忆阻参数的增加能够使处于嵌合态的神经元网络转变为同步态,且耦合强度越大,达到同步态所需要的忆阻参数值越小.进一步探究双层网络的同步性,发现层间耦合强度和忆阻参数的增大有助于网络达到更好的同步.研究结果表明神经元之间的相互作用可以激发双层神经元网络产生多种嵌合态模式,电磁感应可以促进网络由嵌合态向同步态转迁,这些结果有助于理解人脑中复杂的神经放电过程和信息处理机制,并为可能的类脑装置应用提供参考.     

Exponential Lag Synchronization of Memristive Neural Networks with Reaction Diffusion Terms via Neural Activation Function Control and Fuzzy Model

This paper is concerned with the problem of exponential lag synchronization of memristive neural networks with reaction diffusion terms via neural activation function control and fuzzy model. An memristor‐based circuit which exhibits the feature of pinched hysteresis is introduced and further, the memristive neural networks with reaction diffusion terms and such system containing fuzzy model are described at length, respectively. By utilizing the Lyapunov functional method and the neural activation function controller depending on the output of the system in the case of packed circuits, some concise conditions are acquired to guarantee the slave systems exponential lag synchronized with the master systems. Finally, several simulated examples are also presented to demonstrate the correctness of the theoretical results.     

Synchronization of memristive competitive neural networks with different time scales

In this paper, a feedback controller is proposed for the synchronization of memristive competitive neural networks with different time scales. By constructing a proper Lyapunov–Krasovskii functional, as well as employing differential inclusions theory, a feedback controller is designed to achieve the asymptotical synchronization of coupled competitive neural networks. The proposed synchronization algorithm is simple and can be easily realized. A simulation example is given to show the effectiveness of the theoretical results.     

Global Synchronization of Stochastically Disturbed Memristive Neurodynamics via Discontinuous Control Laws

This paper presents the theoretical results on the master-slave (or driving-response) synchronization of two memristive neural networks in the presence of additive noise. First, a control law with a linear time-delay feedback term and a discontinuous feedback term is introduced. By utilizing the stability theory of stochastic differential equations, sufficient conditions are derived for ascertaining global synchronization in mean square using this control law. Second, an adaptive control law consisting of a linear feedback term and a discontinuous feedback term is designed to achieve global synchronization in mean square, and it does not need prior information of network parameters or random disturbances. Finally, simulation results are presented to substantiate the theoretical results.      

Quantized Control for Synchronization of Delayed Fractional-Order Memristive Neural Networks

Neural Processing Letters - This research addresses the synchronization of delayed fractional-order memristive neural networks (DFMNNs) via quantized control. The motivations are twofold: (1) the...     

Complex Projection Synchronization of Fractional-Order Complex-Valued Memristive Neural Networks with Multiple Delays

Neural Processing Letters - In this paper, the complex projection synchronization problem of fractional-order complex-valued memristive neural networks is investigated, in which the projection...     

Prescribed-Time Synchronization of Coupled Memristive Neural Networks with Heterogeneous Impulsive Effects

Neural Processing Letters - This paper is concerned with the prescribed-time synchronization of coupled memristive neural networks (MNNs). The impulsive effects with heterogeneous impulsive...     

Pinning Synchronization of Coupled Memristive Recurrent Neural Networks with Mixed Time-Varying Delays and Perturbations

Neural Processing Letters - In this paper, the pinning synchronization of coupled memristive recurrent neural networks (MNNs) with mixed time-varying delays and perturbations is investigated....     

Exponential Stability and Sampled-Data Synchronization of Delayed Complex-Valued Memristive Neural Networks

Neural Processing Letters - This paper is devoted to the global exponential stability (GES) and synchronization control of delayed complex-valued memristive neural networks (CVMNNs). The criterion...     

Exponential Synchronization of Stochastic Memristive Recurrent Neural Networks Under Alternate State Feedback Control

International Journal of Control, Automation and Systems - This paper solves the exponential synchronization problem of two memristive recurrent neural networks with both stochastic disturbance and...     

Finite/Fixed-Time Synchronization of Delayed Inertial Memristive Neural Networks with Discontinuous Activations and Disturbances

Neural Processing Letters - This paper considers the finite-time and fixed-time synchronization problems for the delayed inertial memristive neural networks (DIMNNs) with disturbances. Different...     

Exponential stabilisation of memristive neural networks under intermittent output feedback control

In this paper, the exponential stabilisation problem is studied for a general class of memristive time-varying delayed neural networks under periodically intermittent output feedback control. First, the periodically intermittent output feedback control rule is designed for the exponential stabilisation of the memristive time-varying delayed neural networks. Then, we derive stabilisation criteria so that the memristive time-varying delayed neural networks are exponentially stable. By the mathematical induction method and constructing suitable Lyapunov–Krasovskii functionals, some easy-to-check criteria are obtained to ensure the exponential stabilisation of memristive time-varying delayed neural networks. Finally, two numerical simulation examples are given to illustrate the validity of the obtained results.     

New Results for Exponential Synchronization of Memristive Cohen–Grossberg Neural Networks with Time-Varying Delays

Neural Processing Letters - This paper concerns the topic of exponential synchronization for a class of memristive Cohen–Grossberg neural networks with time-varying delays by designing a...     

时滞忆阻神经网络动力学分析与控制综述

忆阻器(Memristor)是一种无源的二端电子元件, 同时也是一种纳米级元件, 具有低能耗、高存储、小体积和非易失性等特点. 作为一种新型的存储器件, 忆阻器的研制, 有望使计算机实现人脑特有的信息存储与信息处理一体化的功能, 打破目前冯·诺伊曼(Von Neumann)计算机架构, 为下一代计算机的研制提供一种全新的架构. 鉴于忆阻器与生物神经元突触具有十分相似的功能, 使忆阻器得以充当人工神经元的突触, 建立起一种基于忆阻器的人工神经网络即忆阻神经网络. 忆阻器的问世, 为人工神经网络从电路上模拟人脑提供了可能, 必将极大推动人工智能的发展. 此外, 忆阻神经网络的硬件实现及信号传递过程中, 不可避免会出现时滞与分岔等现象, 因此讨论含各种时滞, 如离散、分布、泄漏时滞以及它们混合的时滞忆阻神经网络系统更具有现实意义. 首先介绍了忆阻器的多种数学模型及其分类, 建立了时滞忆阻神经网络(Delayed memristive neural networks, DMNN)的数学模型并阐述了其优点. 然后提出了处理时滞忆阻神经网络动力学行为与控制问题的两种思路, 详细综述了时滞忆阻神经网络系统的稳定性(镇定)、耗散性与无源性及其同步控制方面的内容, 简述了其他方面的动力学行为与控制, 并介绍了时滞忆阻神经网络动力学行为与控制研究新方向. 最后, 对所述问题进行了总结与展望.     

Exponential Stabilization of Stochastic Memristive Recurrent Neural Networks Under Periodically Intermittent State Feedback Control

In this paper, the exponential stabilization problem is investigated for a class of memristive time‐varying delayed neural networks with stochastic disturbance via periodically intermittent state feedback control. First, a periodically intermittent state feedback control rule is designed for the exponential stabilization of stochastic memristive time‐varying delayed neural networks. Then, by adopting appropriate Lyapunov‐Krasovskii functionals in light of the Lyapunov stability theory, some novel stabilization criteria are obtained to guarantee exponential stabilization of stochastic memristive time‐varying delayed neural networks via periodically intermittent state feedback control. Compared with existing results on stabilization of stochastic memristive time‐varying delayed neural networks, the obtained stabilization criteria in this paper are not difficult to verify. Finally, an illustrative example is given to illustrate the validity of the obtained results.