共查询到20条相似文献，搜索用时 156 毫秒
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以Lorenz混沌系统为例,运用仿真软件Simulink建立了Lorenz混沌系统可视化模型,从中分析了Lorenz系统的混沌特性,最后提出了一种延时反馈控制方法,对Lorcnz混沌系统进行稳定控制,该方法的优．最是全程可视化,不需要采用传统的程序代码和算法进行编程,是研究混沌系统的一种简便、有效的新方法。 相似文献
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CHEN混沌系统的同步控制 总被引：1，自引：0，他引：1
混沌系统的研究在国内外正在广泛的开展,如何使得对混沌系统的控制达到良好的控制效果构成了问题的关键。由于Chen混沌系统具有的参数不确定性,以至于常规的控制方法很难实现对Chert混沌系统的控制,讨论了一种参数可调节的自适应同步设计方法。设计出了可以使两个同结构的Chen系统状态渐近同步的自适应控制器,其参数调节律Lya2punov稳定性理论来确定。最后进行数字仿真,仿真结果表明了这种方法的有效性和实用性。 相似文献
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相空间重构参数选择方法的研究 总被引：2，自引：0，他引：2
基于目前相空间重构中常用的参数选择方法,提出了一种新的相空间重构的联合算法,联合算法以时间窗口法和互信息法为基础,在综合考虑嵌入窗宽的基础上,可同时确定嵌入延迟和嵌入维数.仿真实验表明,用该算法计算Lorenz混沌时间序列关联维相对误差由传统算法的0.83%降低到0.44%,有效地提高了计算相空间重构中不变量的精度. 相似文献
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提出采用Lorenz混沌系统异步跟踪优化期望最大化高斯混合模型算法实现对低信噪比下深度伪装的网络攻击信号最优检测。通过提取待检测网络数据流参数向量和正常数据流参数向量的差值为特征,使用高斯混合模型并与期望最大化算法相结合,设计Lorenz混沌异步跟踪检测算法,对网络数据流进行建模和检测。仿真结果表明改进的检测算法能有效去除不是攻击信号的伪峰,相比Hough变化检测算法,能更加正确地检测非法攻击信号,信噪比为-15dB下,不同异步攻击中的检测概率就能达到100%,实现检测性能最优,尤其适用于信噪比极低的深度伪装网络攻击环境中对攻击信号的检测。研究成果为网络安全防御及应用具有巨大的理论参考价值。 相似文献
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基于局部化(点对或点串)的思想,本文总结了作者近年来在与混沌、熵以及系统回复属性相关的系统复杂性问题方面所取得的进展.解决了Devaney混沌是否蕴含着Li-Yorke混沌这一长时间的公开问题.并说明了"许多"紧度量空间其上存在完全混沌的系统,这些空间包括一些可数的紧度量空间、康托集和任意维的连续统.借助于熵串和序列熵对,刻画了拓扑K系统以及拓扑null系统的结构.最后,使用弱不交性、开覆盖的复杂性函数以及回复时间集对系统回复属性进行了更为细致的分类. 相似文献
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借助Duffing参数敏感性来检测微弱的信号是当前有关领域研究的重点,文章在阐述Duffing系统及其电路实现的基础上,分析基于Duffing混沌系统的电路仿真设计,旨在能够更好的提升电路设计的精准度。 相似文献
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利用分数阶微积分理论,对具有不确定参数的不同混沌系统,设计出了一种分数阶同步器,所设计的系统参数调节律具有分数阶的特性.利用Lorenz系统和一类新的混沌系统的同步仿真验证了此方法的有效性. 相似文献
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The problem of adaptive synchronization for the uncertain chaotic systems with adaptive scaling function is investigated in this paper. In comparison to those of the existing scaling function synchronization, such as the presetting scaling function, the aim of this paper is focused not only on the scaling function but also on the identification of parameters of the chaotic system. Finally, to illustrate the implementation of the proposed method, some numerical simulations are given. 相似文献
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This paper focuses on the problem of chaos control for the permanent magnet synchronous motor with chaotic oscillation, unknown dynamics and time-varying delay by using adaptive sliding mode control based on dynamic surface control. To reveal the mechanism of motor system and facilitate controller design, the dynamic behavior of the system is investigated. Nonlinear items of system model, upper bounds of time delays and their derivatives are taken as unknown in the overall process. A RBF neural network with an adaptive law, which eliminates restrictions on accurate model and parameters, is employed to cope with unknown dynamics. In order to solve issues such as chaotic oscillation, ‘explosion of complexity’ of backstepping, and chattering associated with sliding mode control, a sliding mode controller is developed within the framework of dynamic surface control by the hybrid of adaptive technology and RBF neural network. In addition, an appropriate Lyapunov function is employed to demonstrate the system stability. Finally, the feasibility of the proposed scheme is testified by simulation. 相似文献
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The bi-directionally coupled Lorenz systems are linked to the modeling of a coupled double loop thermosyphon system where the mass momentum and heat exchange are both considered. As the key parameters of the system, known as Rayleigh numbers, increase, the system of differential equations predicts typical flow dynamics in a thermosyphon from heat conduction to time-dependent chaos. In many applications including the thermosyphon systems, there are uncertainties associated with mathematical models such as unmodeled dynamics and parameter variations. Also, under the high heat environment for a thermosyphon, there exist external disturbances quantitatively linked to the Rayleigh numbers. All these sources constitute uncertainties to the dynamical system. Our objective is to design adaptive controllers to stabilize the chaotic flow in each thermosyphon loop with unknown system parameters and existence of uncertainties. The controllers consist of a proportional controller with an adaptive gain and a wavelet network that reconstructs the unknown functions representing the uncertainties. Explicit stability bounds and adaptive laws for the control parameters are obtained so that the coupled Lorenz systems are globally stabilized. 相似文献
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This paper investigates the problem of complete synchronization of chaotic systems with unknown parameters. An adaptive control scheme based on a feedback passivity approach is proposed. The convergence of the synchronization error is guaranteed. The unified chaotic and hyperchaotic Lü systems are taken as illustrative examples. The feasibility and effectiveness of the proposed scheme are demonstrated through numerical simulations. 相似文献
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Sanbo Ding Zhanshan Wang Haisha Niu Huaguang Zhang 《Journal of The Franklin Institute》2017,354(12):4989-5010
Although the drive-response synchronization problem of memristive recurrent neural networks (MRNNs) has been widely investigated, all the existing results are based on the assumption that the parameters of the drive system are known in prior, which are difficult to implement in real-life applications. In the present paper, a Stop and Go adaptive strategy is proposed to investigate the synchronization control of chaotic delayed MRNNs with unknown memristive synaptic weights. Firstly, by defining a series of measurable logical switching signals, a switched response system is constructed. Subsequently, by utilizing the logical switching signals, several suitable parameter update laws are proposed, then some different adaptive controllers are devised to guarantee the synchronization of unknown MRNNs. Since the parameter update laws are weighted by the logical switching signals, they will work or stop automatically with the switch of the unknown weights of drive system. Finally, two numerical examples with their computer simulations are provided to illustrate the effectiveness of the proposed adaptive synchronization schemes. 相似文献
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Yang Chi-Ching 《Journal of The Franklin Institute》2012,349(6):2019-2032
In the presence of system uncertainties, external disturbances and input nonlinearity, this paper is concerned with the adaptive terminal sliding mode controller to achieve synchronization between two identical attractors which belong to a class of second-order chaotic system. The proposed controller with adaptive feedback gains can compensate nonlinear dynamics of the synchronous error system without calculating the magnitudes of them. Meanwhile, these feedback gains are updated by the novel adaptive rules without required that the bounds of system uncertainties and external disturbances have to be known in advance. Some sufficient conditions for stability are provided based on the Lyapunov theorem and numerical studies are performed to verify the effectiveness of presented scheme. 相似文献
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Chia-Nan Ko 《Journal of The Franklin Institute》2012,349(5):1758-1780
This paper proposes a fuzzy neural network (FNN) based on wavelet support vector regression (WSVR) approach for system identification, in which an annealing robust learning algorithm (ARLA) is adopted to adjust the parameters of the WSVR-based FNN (WSVR-FNN). In the WSVR-FNN, first, the WSVR method with a wavelet kernel function is used to determine the number of fuzzy rules and the initial parameters of FNN. After initialization, the adjustment for the parameters of FNNs is performed by the ARLA. Combining the self-learning ability of neural networks, the compact support of wavelet functions, the adaptive ability of fuzzy logic, and the robust learning capability of ARLA, the proposed FNN has the superiority among the several existed FNNs. To demonstrate the performance of the WSVR-FNN, two nonlinear dynamic plants and a chaotic system taken from the extant literature are considered to illustrate the system identification. From the simulation results, it shows that the proposed WSVR-FNN has the superiority over several presented FNNs even the number of training parameters is considerably small. 相似文献
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In this paper, an adaptive TSK-type CMAC neural control (ATCNC) system via sliding-mode approach is proposed for the chaotic symmetric gyro. The proposed ATCNC system is composed of a neural controller and a supervisory compensator. The neural controller uses a TSK-type CMAC neural network (TCNN) to approximate an ideal controller and the supervisory compensator is designed to guarantee system stable in the Lyapunov stability theorem. The developed TCNN provides more powerful representation than the traditional CMAC neural network. Moreover, all the control parameters of the proposed ATCNC system are evolved in the Lyapunov sense to ensure the system stability with a proportional–integral (PI) type adaptation tuning mechanism. Some simulations are presented to confirm the validity of the proposed ATCNC scheme without the occurrence of chattering phenomena. Further, the proposed PI type adaptation laws can achieve faster convergence of the tracking error than that using integral type adaptation laws in previous published papers. 相似文献
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Synchronization of two identical chaotic systems with matched and mismatched perturbations by utilizing adaptive sliding mode control (ASMC) technique is presented in this paper. The sliding surface function is specially designed based on the Lyapunov stability theorem and linear matrix inequality (LMI) optimization technique. The designed tracking controller can not only suppress the mismatched perturbations when the controlled dynamics (master–slave) are in the sliding mode, but also drive the trajectories of synchronization errors into a small bounded region whose size can be adjusted through the designed parameters. Adaptive mechanisms are employed in the proposed control scheme for adapting the unknown upper bounds of the perturbations, and the stability of overall controlled synchronization systems is guaranteed. The comparison of the proposed chaotic synchronization technique with an existing generalized chaotic synchronization (GCS) method as well as application of the proposed control method to secure communications is also demonstrated in this paper. 相似文献
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Farid Tajaddodianfar 《Journal of The Franklin Institute》2018,355(14):6435-6453
This paper proposes anti-oscillation and chaos control scheme for the fractional-order brushless DC motor system wherein there exist unknown dynamics, immeasurable states and chaotic oscillation. Aimed at immeasurable states, the high-gain observers with fast convergence are presented to obtain the information of system states. To compensate uncertainties existing in the dynamic system, a finite-time echo state network with a weight is proposed to approximate uncertain dynamics while its weight is tuned by a fractional-order adaptive law online. Meanwhile a fractional-order filter is introduced to deal with the repeated derivative of the backstepping. Based on the fractional-order Lyapunov stability criterion, the anti-oscillation and chaos control scheme integrated with a high-gain observer, an echo state network and a filter are proposed by using recursive steps of backstepping. The proposed scheme guarantees the boundedness of all signals of the closed-loop system in the sense of global asymptotic stability, and also suppresses chaotic oscillation. Finally, the effectiveness of our scheme is demonstrated by simulation results. 相似文献