忆阻混沌系统的有限时间控制

忆阻器作为电路中的第四个基本元件,于1971年由蔡教授提出。忆阻器是非线性的,具有很多特殊的属性。目前,基于忆阻器的电路设计成为研究热点,基于忆阻器的混沌系统也吸引了很多学者的关注。基于有限时间稳定的思想,研究了一类五阶忆阻混沌系统的全局稳定性及有限时间镇定问题,设计了非连续的状态反馈控制器,使得忆阻混沌系统全局指数稳定。仿真实验结果验证了该方法的正确性和有效性。     

变时滞随机忆阻器神经网络的同步控制

在实现实际的复杂人工神经网络模型以及大规模集成电路时,随机噪声是不可避免的.因此,随机忆阻器神经网络具有重要的现实研究意义.针对变时滞随机忆阻器神经网络的同步控制问题,基于非光滑分析以及集值映射、随机微分包含的理论,利用Lyapunov函数和基本不等式的方法,设计了一个线性反馈控制器.通过恰当选择控制器增益,实现了随机忆阻器神经网络驱动系统与相应的响应系统之间的指数同步,所得到的结果保守性更小.最后,给出数值例子验证了理论结果的有效性.     

基于忆阻器的连续学习混沌神经网络

忆阻器具有独特的记忆功能和连续可变的电导状态,在人工智能与神经网络等研究领域具有巨大的应用优势.详细推导了忆阻器的电荷控制模型,将纳米忆阻器与具有智能信息处理能力的混沌神经网络相结合,提出了一种新型的基于忆阻器的连续学习混沌神经网络模型.利用忆阻器可直接实现网络中繁多的反馈与迭代,即完成外部输入对神经元及神经元之间相互作用的时空总和.提出的忆阻连续学习混沌神经网络可以实现对已知模式和未知模式的区分,并能对未知模式进行自动学习和记忆.给出的计算机仿真验证了方案的可行性.由于忆阻器具有纳米级尺寸和自动的记忆能力,该方案有望大大简化混沌神经网络结构.     

LC振荡型忆阻混沌电路及时滞反馈控制

将磁控忆阻器耦合于LC振荡电路中,得到了一种新的忆阻混沌电路.随后通过理论上的动力学分析、数值仿真、电路实验等验证了该电路的混沌特性.为了实现电路的混沌控制,设计了一种新型模拟时滞控制器.利用该控制器将混沌电路状态变量加以延时并反馈至原电路中.数值仿真和电路实验结果均表明,所设计的时滞控制器可实现混沌电路的镇定控制.进一步研究时滞控制下电路的分岔行为,发现时滞控制下的电路又可通过倍周期分岔进入超混沌.     

时滞混沌系统的同步与反同步及其在保密通信中的应用

根据Lyapunov稳定性理论结合线性矩阵不等式技巧,研究了一类时滞混沌神经网络的同步与反同步问题,设计了各自的状态反馈控制器,并从理论上实现了此类时滞混沌系统的同步与反同步。最后将此类混沌系统应用于保密通信,通过同步与反同步系统的切换,设计出具体的数字保密通信方案,数值仿真验证了该方法的有效性。     

一类变时滞忆阻器递归神经网络全局指数周期性

忆阻器是近几年来提出的一种区别于电阻、电容、电感的一类非线性两端无源电子元件,而忆阻器递归神经网络由于系统参数的不同,系统表现出各种动态性能。针对一类变时滞忆阻器递归神经网络,研究全局指数周期性问题,考虑连接权值在切换状态下的对称和非对称的情况,通过构造两个Lyapunov函数、Halanay不等式和由Fillippov给出的右端不连续微分方程理论的研究方法,提出关于全局指数周期性的充分性条件。最后,实验结果验证了所提理论的可行性和有效性。     

忆阻在混沌电路中的应用综述

忆阻是被认为是除电阻、电容、电感外的第四种基本电路元件.具有记忆功能的非线性电阻.作为基本元件的忆阻器出现,必将导致电子电路的结构体系、原理、设计理论的变革,并促进电子行业新的应用领域的发展.本文从忆阻在混沌系统中的应用来介绍忆阻的应用现状.先介绍忆阻特性和原理,然后引入忆阻器应用在混沌领域的研究成果以及电路仿真忆阻电路.     

一类混合延迟混沌神经网络的同步

时滞混沌神经网络系统是解空间为无穷维系统,可生成多个正向Lyapunov指数,产生具有高度随机性和不可预测性的混沌甚至超混沌序列,这种特性使得时滞混沌神经网络系统特别适用于保密通信中,混沌同步是保密通信中的关键技术。基于Lyapunov稳定性理论和线性矩阵不等式(LMI)方法,研究了一类具有时变延迟和分布式延迟的混沌神经网络系统的同步问题,考虑系统的内部参数不确定性和外部干扰及混合时滞等因素,将系统时滞项加入所设计的控制器中,给出了保证误差系统的全局均方渐近稳定的充分条件和控制律,实现驱动系统和响应系统的同步。与其它方法相比,所设计的含有时滞项的控制器提高了系统误差精度及反应速率。最后,通过仿真实例,验证了所提方法的有效性。     

一种基于荷控忆阻器的混沌电路

在原有忆阻器的定义上采用一种相对简单的荷控忆阻器模型,其忆阻M与电荷q的关系可以用一条二次曲线来描述。经仿真分析,其伏安特性曲线是一条类斜"8"字滞后回线,且会随着周期双极性输入信号的频率和振幅的变化而变化,并在一定程度上受到忆阻器本身参数的影响。用此荷控忆阻器代替蔡氏电路中的蔡氏二极管,得到含荷控忆阻器的电路,给出相轨图、Lyapunov指数与维数来验证其在一定参数配置下处于混沌状态。通过变换系统的初始值,验证了此混沌系统的运动轨迹在初始值微小的变化下会发生很大的差异。Lyapunov指数谱表明含荷控忆阻器的混沌系统在初始值变化时能够进入超混沌状态。利用劳斯判据判别了排除零特征根的影响下该混沌系统在平衡点处的稳定性。     

一种基于忆阻特性的监督神经网络算法及电路设计

针对如何将忆阻器融入人工神经网络算法并进行硬件实现的问题,提出了一种在现场可编程逻辑门阵列(FPGA)平台上实现的基于忆阻特性的监督神经网络算法。该设计以忆阻器模块作为神经网络中的权值存储模块,构建误差反馈机制的监督学习。将该忆阻神经网络电路应用于图像分类问题,并进行了资源占用和处理速度的优化。实验结果表明其分类结果良好,在Cyclone Ⅱ:EP2C70F896I8平台上,整体网络算法占用11 773个逻辑单元(LEs),训练耗时0. 33 ms,图像的测试耗时10μs。这一工作对忆阻器和神经网络的结合提出了一个有益的参考。     

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.     

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.     

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.

     

随机混沌时滞神经网络的指数同步

研究受随机扰动且具有时变时滞神经网络的指数同步. 根据Lyapunov稳定性理论结合线性矩阵不等式技巧, 通过构造含时滞的状态反馈控制器, 使得受到随机扰动的驱动系统和响应系统达到指数同步, 给出了随机时滞神经网络指数同步的新判据, 最后通过仿真验证了所用方法的有效性.     

Synchronization of Non-Identical Unknown Chaotic Delayed Neural Networks Based on Adaptive Sliding Mode Control

The synchronization problem is studied in this paper for non-identical chaotic neural networks with time delays and fully unknown parameters, where the mismatched parameters, activation functions and neural network architectures are taken into account. To overcome the difficulty that complete synchronization of non-identical chaotic neural networks cannot be achieved only by utilizing output feedback control, we design an adaptive sliding mode controller to realize the synchronization. Our synchronization criteria are easily verified and do not need to solve any linear matrix inequality. These results generalize a few previous known results and remove some restrictions on the parameters, activation functions and neural network architectures. This paper also presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed scheme.     

Exponential synchronization of Cohen-Grossberg neural networks via periodically intermittent control

In this paper, a class of Cohen-Grossberg neural networks with time-varying delays are studied by designing a periodically intermittent controller. Some novel and effective exponential synchronization criteria are derived by applying some analysis techniques. These results generalize a few previous known results and remove some restrictions on control width and time-delays. Finally, a chaotic Cohen-Grossberg neural network is represented to show the effectiveness and feasibility of our results.     

Exponential Synchronization of Stochastic Fuzzy Cellular Neural Networks with Reaction-Diffusion Terms via Periodically Intermittent Control

This paper considers the exponential synchronization of stochastic fuzzy cellular neural networks with time-varying delays and reaction-diffusion terms based on p-norm. Motivated by the achievements from both the stability of fuzzy cellular neural networks with stochastic perturbation and reaction-diffusion effects and the synchronization issue of coupled chaotic delayed neural networks by using periodically intermittent control approach, a periodically intermittent controller is proposed to guarantee the exponential synchronization of the coupled chaotic neural networks by using Lyapunov stability theory and stochastic analysis approaches. The synchronization results presented in this paper generalize and improve many known results. This paper also presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed scheme.     

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.     

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.