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．     

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...     

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.     

Memory Analysis for Memristors and Memristive Recurrent Neural Networks

Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational amplifiers. Memristive neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis, i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors' mathematical models with linear and nonlinear drift. Secondly, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.      

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。     

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.     

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.      

基于改进的FNNs移动机械臂输出反馈跟踪控制

针对包含电机动态模型的移动机械臂系统,提出一种鲁棒自适应输出反馈控制方法.将误差符号函数鲁棒积分反馈与神经网络前馈结构相结合用于控制器的设计,然后利用神经网络去逼近机器人和电机系统的不确定项,设计鲁棒项实时补偿网络误差.通过Lyapunov稳定性分析证明闭环系统所有信号半全局一致有界.最后仿真实验表明,控制方法对系统动态不确定性和外界干扰有很好的鲁棒性,可实现移动机械臂的输出反馈跟踪控制.     

回归神经网络中样本特征记忆的反馈控制方法研究

分析了具有遗忘特性及信息锁存能力的状态回归神经网络的计算方法。针对多输入多输出时序样本,提出了更能反映网络短时记忆能力以及时序样本数据物理特性的同时刻反馈控制和计算方法。实验结果显示,该文提出的方法对时序样本的学习和记忆不但具有更高的准确性,而且不增加计算的复杂性。     

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.     

具有信道时延的混沌神经网络脉冲同步

由于控制脉冲只是在特定的时间序列产生,使得同步系统中所需的驱动系统信息和能量减少,从而给混沌系统同步设计带来巨大方便。但是,由于实际电路中器件的切换速度有限且内在通讯需要时间。在通信网络中将不可避免地产生时延。因此,现有的一些同步方法将无法实现。针对这种情况,提出了一种基于脉冲控制的混沌神经网络同步策略。在该策略中考虑了信道时延带来的影响,并设计了控制器实现两个混沌神经网络的同步。计算机仿真结果验证了该方法的可行性和有效性。     

Synchronization of Switched Neural Networks with Time‐varying Delays via Sampled‐data Control

This paper investigates sampled‐data synchronization control of switched neural networks with time‐varying delays under average dwell time. Based on the delay system method, the sampled‐data synchronization system is proposed with time‐varying delays and input delays in the unified framework for switched neural networks. By constructing a suitable Lyapunov‐Krasovskii functional and free‐weighting matrix, the relationship between the average dwell time and the maximum sampling interval is revealed to form delay‐dependent exponentially synchronization criteria. The desired mode‐dependent controller under the maximum sampling interval and decay rate is designed. Finally, two numerical examples are provided to demonstrate the effectiveness and feasibility of the proposed techniques.     

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.     

Control and Synchronization Of A Class Of Uncertain Fractional Order Chaotic Systems Via Adaptive Backstepping Control

This paper presents the stabilization and synchronization problem of a class of fractional order chaotic systems with unknown parameters. A systematic step by step approach is explained to derive control results using an adaptive backstepping strategy. The analytically obtained control structure, derived by blending a systematic backstepping procedure with Mittag‐Leffler stability results, helps in obtaining the stability of a strict feedback‐like class of uncertain fractional order chaotic systems. The results are further extended to achieve synchronization of these systems in master–slave configuration. Thereafter, the methodology has been applied to two example systems, that is, chaotic Chua's circuit and Genesio‐Tesi system, which belong to addressed class, in order to show the application of results. Numerical simulation given at the end confirms the efficacy of the scheme presented here.     

统一混沌系统的输出反馈控制器设计

利用输出反馈研究了一类统一混沌系统平衡点渐进稳定问题.基于李亚普诺夫稳定理论,线性矩阵不等式方法及不等式技巧,分别用两输入单输出和单输入单输出反馈设计控制器,解决了状态不可测情况下统一混沌系统的控制问题.即使得混沌系统的状态渐进趋于给定的任一平衡点.与现有文献结果相比,所设计的控制器,反馈增益小,结构简单,保守性小.最后以Lorenz系统为例作了仿真,仿真结果表明了该控制器的有效性.     

结合模糊控制的时序数据预测循环神经网络

时序数据处理任务中,循环神经网络模型以及相关衍生模型有较好的性能,如长短期记忆模型(LSTM),门限循环单元(GRU)等.模型的记忆层能够保存每个时间步的信息,但是无法高效处理某些领域的时序数据中的非等时间间隔和不规律的数据波动,如金融数据.本文提出了一种基于模糊控制的新型门限循环单元(GRU-Fuzzy)来解决这些问...     

基于输出反馈滑模控制的分数阶超混沌系统同步

以具有更大秘钥空间的分数阶超混沌系统为驱动系统和响应系统,利用具有实际应用意义的输出反馈滑模控制实现两个系统的同步.通过对同步误差系统方程进行结构分解,在辅助系统的基础上设计具有输出反馈特性的滑模控制律.在分数阶系统稳定性理论基础上利用MATLAB YALMIP工具箱对滑模参数进行整定,并利用分数阶Lyapunov稳定性定理证明了滑模控制律和自适应滑模控制律的稳定性.最后,数值仿真表明了本文方法的有效性和可行性.     

Synchronization for an array of coupled stochastic discrete-time neural networks with mixed delays

In this paper, a synchronization problem is investigated for an array of coupled stochastic discrete-time neural networks with both discrete and distributed time-varying delays. By utilizing a novel Lyapunov function and the Kronecker product, it is shown that the addressed stochastic discrete-time neural networks is synchronized if certain linear matrix inequalities (LMIs) are feasible. Neither any model transformation nor free-weighting matrices are employed in the derivation of the results obtained, and they can be solved efficiently via the Matlab LMI Toolbox. The proposed synchronization criteria are less conservative than some recently known ones in the literature, which is demonstrated via two numerical examples.     

基于递归神经网络的一类非线性无模型系统的自适应控制

给出了基于递归神经网络非线性无模型的自适应控制方案，它具有灵活、简单、方法等特点，可以处理传统方法和非线性无模型系统自适应控制方法不能控制或控制效果不理想的非线性对象。理论分析和仿真结果证明了这种方法的优越性。     

基于神经网络误差修正的混沌控制与同步

针对混沌系统非线性强、多变量耦合等特点,提出了一种基于神经网络误差修正的自适应多变量混沌系统的广义预测控制算法,用线性广义预测控制器控制混沌系统,用神经网络对模型预测误差进行修正。算法中辩识过程模型用递推最小二乘法(RLS)、神经网络权值用Davidon最小二乘法(DLS)训练。这种算法对被控混沌系统的先验知识要求较少,无需知道被控系统的精确模型,数值仿真显示可实现混沌系统的宽范围控制与同步。