Adaptive synchronization of neural networks with or without time-varying delay

In this paper, based on the invariant principle of functional differential equations, a simple, analytical, and rigorous adaptive feedback scheme is proposed for the synchronization of almost all kinds of coupled identical neural networks with time-varying delay, which can be chaotic, periodic, etc. We do not assume that the concrete values of the connection weight matrix and the delayed connection weight matrix are known. We show that two coupled identical neural networks with or without time-varying delay can achieve synchronization by enhancing the coupling strength dynamically. The update gain of coupling strength can be properly chosen to adjust the speed of achieving synchronization. Also, it is quite robust against the effect of noise and simple to implement in practice. In addition, numerical simulations are given to show the effectiveness of the proposed synchronization method.     

Adaptive synchronization of a class of chaotic neural networks with known or unknown parameters

In this Letter, synchronization of a class of chaotic neural networks with known or unknown parameters is investigated. By combing the adaptive control and linear feedback with update law, a simple, analytical, and rigorous adaptive feedback scheme is derived to achieve synchronization of two coupled neural networks with time-varying delay based on the invariant principle of functional differential equations and parameter identification. With this method, parameter identification and synchronization can be achieved simultaneously. Simulation results are given to justify the theoretical analysis.     

基于神经网络和滑模控制的不确定混沌系统同步

基于滑模控制技术和径向基函数神经网络,设计出一种神经滑模控制器,实现了两个不确定混沌系统的同步.控制器的设计不依赖于系统的数学模型,只与系统的输出状态有关,而且对参数不确定性和外界干扰具有较强的稳健性.最后,利用本方法设计出控制器实现了未知Lorenz系统的自同步、未知Lorenz系统与Chen系统之间的异结构同步,而且响应时间短,同步效果好. 关键词： 混沌同步 滑模控制 神经网络 不确定混沌系统     

一类混沌神经网络的全局同步

研究了一类时滞混沌神经网络的全局同步问题.应用驱动-响应同步方法和线性矩阵不等式技术，给出了时滞混沌神经网络全局同步的充分条件和同步控制器设计方法，而且所得到的控制器易于实现.仿真示例验证了本文方法的有效性. 关键词： 混沌神经网络 同步 驱动-响应法 线性矩阵不等式     

LMI-based robust adaptive synchronization of FitzHugh-Nagumo neurons with unknown parameters under uncertain external electrical stimulation

This Letter addresses a nonlinear robust adaptive control that utilizes linear matrix inequalities for asymptotic synchronization of two coupled chaotic FitzHugh-Nagumo neurons under unknown parameters and uncertain stimulation current amplitudes and phase shifts. Synchronization of chaotic neurons using the proposed control method through numerical simulation is demonstrated.     

多涡卷混沌系统的广义同步控制

基于反步自适应方法,提出了一种在初值不同以及驱动系统参数未知的情况下,保持驱动和响应两多涡卷混沌系统同步的控制律设计方法.该方法所设计的控制律能够同时适用于两混沌系统的完全同步、反同步和一类非线性广义同步,具有较高的实用价值.仿真结果表明了所设计控制律的有效性. 关键词： 多涡卷混沌 广义同步 反步 自适应     

Adaptive complete synchronization of two identical or different chaotic (hyperchaotic) systems with fully unknown parameters

This paper studies the adaptive complete synchronization of chaotic and hyperchaotic systems with fully unknown parameters. In practical situations, some systems' parameters cannot be exactly known a priori, and the uncertainties often affect the stability of the process of synchronization of the chaotic oscillators. An adaptive scheme is proposed to compensate for the effects of parameters' uncertainty based on the structure of chaotic systems in this paper. Based on the Lyapunov stability theorem, an adaptive controller and a parameters update law can be designed for the synchronization of chaotic and hyperchaotic systems. The drive and response systems can be nonidentical, even with different order. Three illustrative examples are given to demonstrate the validity of this technique, and numerical simulations are also given to show the effectiveness of the proposed chaos synchronization method. In addition, this synchronization scheme is quite robust against the effect of noise.     

Robust pre-specified time synchronization of chaotic systems by employing time-varying switching surfaces in the sliding mode control scheme

In the conventional chaos synchronization methods, the time at which two chaotic systems are synchronized, is usually unknown and depends on initial conditions. In this work based on Lyapunov stability theory a sliding mode controller with time-varying switching surfaces is proposed to achieve chaos synchronization at a pre-specified time for the first time. The proposed controller is able to synchronize chaotic systems precisely at any time when we want. Moreover, by choosing the time-varying switching surfaces in a way that the reaching phase is eliminated, the synchronization becomes robust to uncertainties and exogenous disturbances. Simulation results are presented to show the effectiveness of the proposed method of stabilizing and synchronizing chaotic systems with complete robustness to uncertainty and disturbances exactly at a pre-specified time.     

Adaptive synchronization of a class of fractional-order complex-valued chaotic neural network with time-delay

This paper is concerned with the adaptive synchronization of fractional-order complex-valued chaotic neural networks (FOCVCNNs) with time-delay. The chaotic behaviors of a class of fractional-order complex-valued neural network are investigated. Meanwhile, based on the complex-valued inequalities of fractional-order derivatives and the stability theory of fractional-order complex-valued systems, a new adaptive controller and new complex-valued update laws are proposed to construct a synchronization control model for fractional-order complex-valued chaotic neural networks. Finally, the numerical simulation results are presented to illustrate the effectiveness of the developed synchronization scheme.     

Adaptive full state hybrid projective synchronization of chaotic systems with the same and different order

This Letter further investigates the full state hybrid projective synchronization (FSHPS) of chaotic and hyper-chaotic systems with fully unknown parameters. Based on the Lyapunov stability theory, a unified adaptive controller and parameters update law can be designed for achieving the FSHPS of chaotic and/or hyper-chaotic systems with the same and different order. Especially, for two chaotic systems with different order, reduced order MFSHPS (an acronym for modified full state hybrid projective synchronization) and increased order MFSHPS are first studied in this Letter. Five groups numerical simulations are provided to verify the effectiveness of the proposed scheme. In addition, the proposed FSHPS scheme is quite robust against the effect of noise.     

Projective-anticipating, projective, and projective-lag synchronization of time-delayed chaotic systems on random networks

We study projective-anticipating, projective, and projective-lag synchronization of time-delayed chaotic systems on random networks. We relax some limitations of previous work, where projective-anticipating and projective-lag synchronization can be achieved only on two coupled chaotic systems. In this paper, we realize projective-anticipating and projective-lag synchronization on complex dynamical networks composed of a large number of interconnected components. At the same time, although previous work studied projective synchronization on complex dynamical networks, the dynamics of the nodes are coupled partially linear chaotic systems. In this paper, the dynamics of the nodes of the complex networks are time-delayed chaotic systems without the limitation of the partial linearity. Based on the Lyapunov stability theory, we suggest a generic method to achieve the projective-anticipating, projective, and projective-lag synchronization of time-delayed chaotic systems on random dynamical networks, and we find both its existence and sufficient stability conditions. The validity of the proposed method is demonstrated and verified by examining specific examples using Ikeda and Mackey-Glass systems on Erdos-Renyi networks.     

基于量子粒子群算法的混沌系统同步控制及参数辨识

针对异结构且参数未知混沌系统的同步控制,设计同步控制器并对误差系统的稳定性进行理论分析.通过合理选择优化目标函数,利用量子粒子群算法高效的全局寻优能力,实现混沌系统的未知参数辨识,有效降低同步控制器的开发难度.以Rössler混沌系统与Lorenz混沌系统的异结构同步为研究对象,进行数值仿真实验,结果表明该方法能够实现驱动-响应系统的快速混沌同步及系统未知参数的准确辨识,并验证该方法的可行性和有效性.     

参数未知的不同阶数混沌系统广义同步及参数估计

采用扩阶方法(使响应系统和驱动系统的维数相同)，把不同阶数混沌系统的广义同步问题转化为相同阶数混沌系统之间的广义同步，基于Lyapunov稳定性定理和自适应控制方法(用于相同阶数混沌系统的同步)，给出了自适应控制器和参数自适应律，进而实现了不同阶数混沌系统的广义同步.将该方法应用于参数未知的超Lü，Lorenz，广义Lorenz和Liu等系统之间的广义混沌同步，理论证明了该方法可以使这些系统达到渐近广义同步，并且可以辨识驱动系统和响应系统的所有参数，数值模拟进一步证明了该方法的有效性.     

Synchronization Control of Two Different Chaotic Systems with Known and Unknown Parameters

Chaos synchronization of two different chaotic systems with known and unknown parameters is studied. Based on the Lyapunov stability theory, two different chaotic systems with known parameters realize global synchronization via the successfully designed nonlinear controller. By employing an adaptive synchronization scheme, the synchronization of two different chaotic systems with unknown parameters is achieved. Numerical simulations validate the effectiveness of the theoretical analysis.     

不确定分数阶时滞混沌系统自适应神经网络同步控制

针对带有完全未知的非线性不确定项和外界扰动的异结构分数阶时滞混沌系统的同步问题,基于Lyapunov稳定性理论,设计了自适应径向基函数(radial basis function,RBF)神经网络控制器以及整数阶的参数自适应律.该控制器结合了RBF神经网络和自适应控制技术,RBF神经网络用来逼近未知非线性函数,自适应律用于调整控制器中相应的参数.构造平方Lyapunov函数进行稳定性分析,基于Barbalat引理证明了同步误差渐近趋于零.数值仿真结果表明了该控制器的有效性.     

Anti-synchronization of Time-delayed Chaotic Neural Networks Based on Adaptive Control

This paper investigates the adaptive anti-synchronization problem for time-delayed chaotic neural networks with unknown parameters. Based on Lyapunov-Krasovskii stability theory and linear matrix inequality (LMI) approach, the adaptive anti-synchronization controller is designed and an analytic expression of the controller with its adaptive laws of unknown parameters is shown. The proposed controller can be obtained by solving the LMI problem. An illustrative example is given to demonstrate the effectiveness of the proposed method.     

一类参数不确定混沌系统的自适应同步

基于Lyapunov稳定性原理,对一类参数不确定混沌系统,提出一种自适应同步控制方法.给出了自适应同步控制器和参数自适应律的解析表达式,对于具体的误差系统,控制器结构还可以进一步简化.该方法较为简单,适用范围广.以新混沌和超混沌Chen系统为例,数值模拟证明了该方法的有效性和可行性. 关键词： 参数不确定混沌系统 自适应同步 新混沌系统 超混沌Chen系统     

Adaptive synchronization between two different chaotic systems with unknown parameters

A unified mathematical expression describing a class of chaotic systems is presented, for which the problem of adaptive synchronization between two different chaotic systems with unknown parameters has been studied. Based on Lyapunov stability theory, an adaptive synchronization controller is designed and analytic expression of the controller and the adaptive laws of parameters are developed. The adaptive synchronizations between Lorenz and Chen systems, a modified Chua's circuit and Rössler systems are taken as two illustrative examples to show the effectiveness of the proposed method.     

Complete synchronization between two bi-directionally coupled chaotic systems via an adaptive feedback controller

In this paper, we apply a simple adaptive feedback control scheme to synchronize two bi-directionally coupled chaotic systems. Based on the invariance principle of differential equations, sufficient conditions for the global asymptotic synchronization between two bi-directionally coupled chaotic systems via an adaptive feedback controller are given. Unlike other control schemes for bi-directionally coupled systems, this scheme is very simple to implement in practice and need not consider coupling terms. As examples, the autonomous hyperchaotic Chen systems and the new non-autonomous 4D systems are illustrated. Numerical simulations show that the proposed method is effective and robust against the effect of weak noise.     

Robust adaptive fuzzy neural tracking control for a class of unknown chaotic systems

In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for online estimation of the controlled system dynamics by tuning the parameters of fuzzy neural network (FNN). The Gaussian function, a specific example of radial basis function, is adopted here as a membership function. So, the tuning parameters include the weighting factors in the consequent part and the means and variances of the Gaussian membership functions in the antecedent part of fuzzy implications. To tune the parameters online, the back-propagation (BP) algorithm is developed. The robust controller is used to guarantee the stability and to control the performance of the closed-loop adaptive system, which is achieved always. Finally, simulation results show that the AFNC can achieve favourable tracking performances.