How many parameters can be identified by adaptive synchronization in chaotic systems?

Five interesting experiments have been done for a class of chaos synchronization systems with unknown parameters and unknown control directions. And three important conclusions about parameters identification have been made. First, a necessary and sufficient condition for parameters identification is obtained. Second, a Nussbaum method is proposed to solve the problem of unknown control direction. Third, the adaptive method is not infinitely effective considered for our current ability of computation and simulation algorithm.     

General methods for modified projective synchronization of hyperchaotic systems with known or unknown parameters

This work is concerned with the general methods for modified projective synchronization of hyperchaotic systems. A systematic method of active control is developed to synchronize two hyperchaotic systems with known parameters. Moreover, by combining the adaptive control and linear feedback methods, general sufficient conditions for the modified projective synchronization of identical or different chaotic systems with fully unknown or partially unknown parameters are presented. Meanwhile, the speed of parameters identification can be regulated by adjusting adaptive gain matrix. Numerical simulations verify the effectiveness of the proposed methods.     

不确定Chen系统的参数辨识与自适应同步

研究了不确定Chen系统的自适应同步和参数辨识，设计了自适应控制器和参数更新规则，理论证明了该控制器可使得两个Chen系统——驱动系统和未知参数的响应系统渐进地达到同步，并且可以辨识出响应系统的未知参数.数值模拟结果证明了该控制器的有效性. 关键词： 不确定Chen系统 自适应同步 参数辨识 控制器     

Parameters identification and adaptive synchronization of chaotic systems with unknown parameters

Based on Lyapunov stabilization theory, an adaptive controller with parameters identification for a class of chaotic systems with unknown parameters is proposed in this Letter. The proposed control scheme is successfully applied to some typical chaotic systems, which can be spilt into two terms: one is the term with known states, the other is the symmetric matrix term with unknown parameters, such as Lorenz system. And with the proposed adaptive control law, the two unified systems with unknown parameter are also to be synchronized. Simulation results verify the proposed scheme's effectiveness.     

Adaptive lag synchronization and parameters adaptive lag identification of chaotic systems

This Letter investigates the problem of adaptive lag synchronization and parameters adaptive lag identification of chaotic systems. In comparison with those of existing parameters identification schemes, the unknown parameters are identified by adaptive lag laws, and the delay time is also identified in this Letter. Numerical simulations are also given to show the effectiveness of the proposed method.     

Serial identification of EEG patterns using adaptive wavelet-based analysis

A problem of recognition specific oscillatory patterns in the electroencephalograms with the continuous wavelet-transform is discussed. Aiming to improve abilities of the wavelet-based tools we propose a serial adaptive method for sequential identification of EEG patterns such as sleep spindles and spike-wave discharges. This method provides an optimal selection of parameters based on objective functions and enables to extract the most informative features of the recognized structures. Different ways of increasing the quality of patterns recognition within the proposed serial adaptive technique are considered.     

基于Takagi-Sugeno模糊模型的超混沌系统自适应投影同步及参数辨识

研究了不确定超混沌系统的自适应投影同步问题.基于Takagi-Sugeno（T-S）模糊模型,设计了一种自适应模糊控制器和参数更新规则.利用Lyapunov稳定性理论,证明了所提方案可以实现不确定超混沌系统的投影同步,并同时辨识出未知的系统参数.通过对超混沌Lorenz系统的数值仿真实验进一步验证了所提方案的有效性. 关键词： Takagi-Sugeno模糊模型 自适应 投影同步 超混沌     

The adaptive synchronization of fractional-order Liu chaotic system with unknown parameters

In this paper, the chaos control and the synchronization of two fractional-order Liu chaotic systems with unknown parameters are studied. According to the Lyapunov stabilization theory and the adaptive control theorem, the adaptive control rule is obtained for the described error dynamic stabilization. Using the adaptive rule and a proper Lyapunov candidate function, the unknown coefficients of the system are estimated and the stabilization of the synchronizer system is demonstrated. Finally, the numerical simulation illustrates the efficiency of the proposed method in synchronizing two chaotic systems.     

Anti-synchronization of chaotic systems with uncertain parameters via adaptive control

In this Letter, an adaptive control scheme is developed to study the anti-synchronization behavior between two identical and different chaotic systems with unknown parameters. This adaptive anti-synchronization controller is designed based on Lyapunov stability theory and an analytic expression of the controller with its adaptive laws of parameters is shown. The adaptive anti-synchronization between two identical systems (Chen system) and different systems (Genesio and Lü systems) are taken as two illustrative examples to show the effectiveness of the proposed method. Theoretical analysis and numerical simulations are shown to verify the results.     

Parameters identification and adaptive full state hybrid projective synchronization of chaotic (hyper-chaotic) systems

Based on the active control idea and the invariance principle of differential equations, a general scheme of adaptive full state hybrid projective synchronization (FSHPS) and parameters identification of a class of chaotic (hyper-chaotic) systems with linearly dependent uncertain parameters is proposed in this Letter. With this effective scheme parameters identification and FSHPS of chaotic and hyper-chaotic systems can be realized simultaneously. Numerical simulations on the chaotic Chen system and the hyper-chaotic Chen system are presented to verify the effectiveness of the proposed scheme.     

不确定Chua’s电路的参数辨识与自适应同步

针对含有不确定参数的Chua’s电路,提出一种仅需传递单路信号实现混沌自适应同步的方法. 基于Lyapunov稳定性理论证明了该方法可使得两个Chua系统——驱动系统和未知参数的响应系统渐近地达到同步,并且可以辨识出响应系统的未知参数. 数值计算结果表明了该方法的有效性. 关键词： 不确定Chua’s电路 混沌同步 参数辨识 Lyapunov函数     

自适应同步参数未知的异结构分数阶超混沌系统

基于分数阶系统稳定理论,实现了分数阶超混沌CYQY系统与参数未知的分数阶超混沌Lorenz系统间的异结构自适应同步.不仅设计了控制器,还设计了参数自适应规则并保留了非线性项.数值仿真证实了自适应控制器的有效性. 关键词： 分数阶 超混沌 同步 自适应     

Topology identification of the complex networks with non-delayed and delayed coupling

In practical situation, there exists many uncertain information in complex networks, such as the topological structures. So the topology identification is an important issue in the research of the complex networks. Based on LaSalle's invariance principle, in this Letter, an adaptive controlling method is proposed to identify the topology of a weighted general complex network model with non-delayed and delayed coupling. Finally, simulation results show that the method is effective.     

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.     

Parameters identification and synchronization of chaotic systems based upon adaptive control

A novel control method is proposed for a class of chaotic systems dependent linearly on unknown parameters based upon adaptive control. With this method parameters identification and synchronization of chaotic systems can be achieved simultaneously. Simulation results with Lorentz system and Chen's system are presented to show the effectiveness of the approach.     

Time correlations and persistence probability of a Brownian particle in a shear flow

This paper presents an adaptive lag synchronization based method for simultaneous identification of topology and parameters of uncertain general complex dynamical networks with and without time delays. Based on Lyapunov stability theorem and LaSalle??s invariance principle, an adaptive controller is designed to realize lag synchronization between drive and response systems, meanwhile, identification criteria of network topology and system parameters are obtained. Numerical simulations illustrate the effectiveness of the proposed method.     

Robust Blind Adaptive Channel Equalization in Chaotic Communication Systems

Based on the bounded property and statistics of chaotic signal and the idea of set-membership identification, we propose a set-membership generalized least mean square （SM-GLMS） algorithm with variable step size for blind adaptive channel equalization in chaotic communication systems. The steady state performance of the proposed SM-GLMS algorithm is analysed, and comparison with an extended Kalman filter （EKF）-based adaptive algorithm and variable gain least mean square （VG-LMS） algorithm is performed for blind adaptive channel equalization. Simulations show that the proposed SM-GLMS algorithm can provide more significant steady state performance improvement than the EKF-based adaptive algorithm and VG-LMS algorithm.     

Periodic synchronization of community networks with non-identical nodes uncertain parameters and adaptive coupling strength

In this paper, we propose a novel approach for simultaneously identifying unknown parameters and synchronizing time-delayed complex community networks with nonidentical nodes. Based on the LaSalle＇s invariance principle, a cri- teflon is established by constructing an effective control identification scheme and adjusting automatically the adaptive coupling strength. The proposed control law is applied to a complex community network which is periodically synchro- nized with different chaotic states. Numerical simulations are conducted to demonstrate the feasibility of the proposed method.     

Generalized projective synchronization of a class of chaotic (hyperchaotic) systems with uncertain parameters

In this paper is investigated the generalized projective synchronization of a class of chaotic (or hyperchaotic) systems, in which certain parameters can be separated from uncertain parameters. Based on the adaptive technique, the globally generalized projective synchronization of two identical chaotic (hyperchaotic) systems is achieved by designing a novel nonlinear controller. Furthermore, the parameter identification is realized simultaneously. A sufficient condition for the globally projective synchronization is obtained. Finally, by taking the hyperchaotic Lü system as example, some numerical simulations are provided to demonstrate the effectiveness and feasibility of the proposed technique.     

Topology Identification of General Dynamical Network with Distributed Time Delays

Genera/dynamical networks with distributed time delays are studied. The topology of the networks are viewed as unknown parameters, which need to be identified. Some auxiliary systems （also called the network estimators） are designed to achieve this goal. Both linear feedback control and adaptive strategy are applied in designing these network estimators. Based on linear matrix inequalities and the Lyapunov function method, the sufficient condition for the achievement of topology identification is obtained. This method can also better monitor the switching topology of dynamical networks. Illustrative examples are provided to show the effectiveness of this method.