混沌神经网络逆控制的同步及其在保密通信系统中的应用

利用神经网络的学习、逼近能力构造混沌神经网络，提出逆控制混沌同步方法来同步两个混沌神经网络，并基于逆控制和混沌神经网络的同步给出一种新的混沌保密通信系统.理论分析和数值实验结果表明，新系统能够有效地克服信道噪声对信息传输的不良影响，具有较强通用性和柔韧性，且有同步速度快，信号恢复精度高和密钥量大的优点. 关键词： 混沌同步 自适应逆控制 混沌神经网络 保密通信     

一种预测混沌时间序列的模糊神经网络方法

给出了一种预测混沌时间序列的模糊神经网络及其学习方法，给出的方法能直接从数据中提取模糊规则，经过优化得到最佳模糊规则库，并利用神经网络的自学习功能修改隶属函数的参数和网络的权值，减少了规则的匹配过程，加快了推理速度，增强了网络的自适应能力. 使用该神经网络及其学习方法对Lorenz混沌时间序列进行了预测仿真研究，试验结果表明给出的预测工具和方法是有效的. 关键词： 模糊神经网络 模糊规则提取 混沌时间序列预测     

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     

混沌的模糊神经网络逆系统控制

提出用Sugeno型的模糊推理神经网络建立混沌系统的逆系统模型,并采用逆系统方法进行混沌的控制.这种方法的特点是可以不必建立混沌系统的解析模型,通过模糊神经网络学习混沌系统的运动规律,通过学习获得的规律对混沌进行有效的控制,并且该控制方法可以控制混沌系统以一定精度跟踪连续变化的给定信号.理论分析及针对虫口模型和Henon模型仿真研究证明了该方法的有效性 关键词： 混沌 模糊神经网络 逆系统控制     

不确定时滞混沌系统的自适应动态神经网络控制

研究了一类具有不确定时滞的非自治混沌系统的控制问题. 通过结合Lyapunov-Krasovskii函数和Lyapunov函数设计参数可调的不确定时滞补偿器,使得反馈控制输入信号不受时延的影响;同时引入动态结构自适应神经网络,以消除系统的不确定性,其隐层神经元的个数可以随着逼近误差的增大而自适应增加,改善了逼近速度与网络复杂度的关系;最后,用Duffing混沌系统的控制仿真示例表明该方法的有效性. 关键词： 混沌系统 自适应控制 不确定时滞 动态结构神经网络     

耦合发电机系统的自适应控制与同步

讨论了耦合发电机系统的自适应控制和参数未知时的自适应同步问题.设计了自适应控制器，将耦合发电机系统的混沌轨道镇定到平衡点，并使得两个参数未知的耦合发电机系统达到了混沌同步.数值模拟验证了所设计的控制器的有效性. 关键词： 耦合发电机系统 自适应控制 平衡点 同步     

Liu混沌系统的非线性反馈同步控制

研究了新型混沌系统——Liu系统的同步控制问题.基于Lyapunov稳定性理论，采用非线性反馈控制方法，给出了Liu系统实现自同步的充分条件以及控制律参数的取值范围；结合参数自适应控制方法，实现了Liu混沌系统与统一混沌系统的异结构系统快速同步.数值仿真证明了该方法的有效性. 关键词： Liu混沌系统 混沌同步 非线性反馈控制 参数自适应控制     

混沌时间序列的模糊神经网络预测

设计一种新型混合模糊神经推理系统，该系统仅从期望输入输出数据集即可达到获取知识、确定模糊初始规则基的目的.再利用神经网络学习能力便不难修改规则库中的模糊规则以及隶属函数和网络权值等参数，这样大大减少了规则匹配过程，加快了推理速度，从而极大程度地提高了系统的自适应能力.用它对Mackey-Glass混沌时间序列进行预测试验,结果表明利用该网络模型无论离线还是在线学习均能对Mackey-Glass混沌时间序列进行准确的预测，证明了该系统的有效性. 关键词： 神经网络模型 模糊逻辑 混合推理系统 混沌时间序列     

随机性参数自适应的混沌同步

对两个不同参数的混沌系统进行随机性参数自适应控制，选取合适的控制律和反馈系数，导致其同步.以Henon映射为例进行数值模拟，结果表明，由于控制周期和反馈系数的随机变化，具有一定的实用意义. 关键词： Henon映射 混沌同步 随机性自适应控制     

带未知扰动的多涡卷混沌系统修正函数时滞投影同步

研究了带有未知外部扰动的不同多涡卷混沌系统修正函数时滞投影同步问题. 基于Lyapunov稳定性理论, 采用模糊自适应控制的方法设计自适应同步控制器及参数的更新规则. 该控制器在实现混沌系统修正函数时滞投影同步的同时对外界扰动的变化能保持较好的稳定性. 数值仿真的结果进一步验证了该方法的有效性.     

含不确定性混沌系统的模糊自适应同步

研究了含不确定性混沌系统的同步问题.基于Takagi-Sugeno(T-S)模糊动态模型，给出了一个新的自适应模糊同步控制设计方法.该方法同时适用于相同结构混沌系统的同步以及异构混沌系统的同步.为说明问题，给出了Lorenz混沌系统和Rossler混沌系统的同步控制设计和仿真结果. 关键词： 混沌系统 模糊控制 同步     

Adaptive Event-Triggered Synchronization of Uncertain Fractional Order Neural Networks with Double Deception Attacks and Time-Varying Delay

This paper investigates the problem of adaptive event-triggered synchronization for uncertain FNNs subject to double deception attacks and time-varying delay. During network transmission, a practical deception attack phenomenon in FNNs should be considered; that is, we investigated the situation in which the attack occurs via both communication channels, from S-C and from C-A simultaneously, rather than considering only one, as in many papers; and the double attacks are described by high-level Markov processes rather than simple random variables. To further reduce network load, an advanced AETS with an adaptive threshold coefficient was first used in FNNs to deal with deception attacks. Moreover, given the engineering background, uncertain parameters and time-varying delay were also considered, and a feedback control scheme was adopted. Based on the above, a unique closed-loop synchronization error system was constructed. Sufficient conditions that guarantee the stability of the closed-loop system are ensured by the Lyapunov-Krasovskii functional method. Finally, a numerical example is presented to verify the effectiveness of the proposed method.     

基于自适应模糊控制的分数阶混沌系统同步

本文主要研究了带有未知外界扰动的分数阶混沌系统的同步问题.基于分数阶Lyapunov稳定性理论,构造了分数阶的参数自适应规则以及模糊自适应同步控制器.在稳定性分析中主要使用了平方Lyapunov函数.该控制方法可以实现两分数阶混沌系统的同步,使得同步误差渐近趋于0.最后,数值仿真结果验证了本文方法的有效性.     

Fuzzy adaptive synchronization of uncertain chaotic systems

This Letter presents an adaptive approach for synchronization of Takagi–Sugeno (T–S) fuzzy chaotic systems. Since the parameters of chaotic system are assumed unknown, the adaptive law is derived to estimate the unknown parameters and its stability is guaranteed by Lyapunov stability theory. The control law to be designed consists of two parts: one part that can stabilize the synchronization error dynamics and the other part that estimates the unknown parameters. Numerical examples are given to demonstrate the validity of the proposed adaptive synchronization approach.     

Controlling fractional order chaotic systems based on Takagi-Sugeno fuzzy model and adaptive adjustment mechanism

In this Letter, a kind of novel model, called the generalized Takagi-Sugeno (T-S) fuzzy model, is first developed by extending the conventional T-S fuzzy model. Then, a simple but efficient method to control fractional order chaotic systems is proposed using the generalized T-S fuzzy model and adaptive adjustment mechanism (AAM). Sufficient conditions are derived to guarantee chaos control from the stability criterion of linear fractional order systems. The proposed approach offers a systematic design procedure for stabilizing a large class of fractional order chaotic systems from the literature about chaos research. The effectiveness of the approach is tested on fractional order Rössler system and fractional order Lorenz system.     

Adaptive fuzzy sliding mode control of smart structures

Smart structures are usually designed with a stimulus-response mechanism to mimic the autoregulatory process of living systems. In this work, in order to simulate this natural and self-adjustable behavior, an adaptive fuzzy sliding mode controller is applied to a shape memory two-bar truss. This structural system exhibits both constitutive and geometrical nonlinearities presenting the snap-through behavior and chaotic dynamics. On this basis, a variable structure controller is employed for vibration suppression in the chaotic smart truss. The control scheme is primarily based on sliding mode methodology and enhanced by an adaptive fuzzy inference system to cope with modeling inaccuracies and external disturbances. The robustness of this approach against both structured and unstructured uncertainties enables the adoption of simple constitutive models for control purposes. The overall control system performance is evaluated by means of numerical simulations, promoting vibration reduction and avoiding snap-through behavior.     

Controlling chaos using Takagi--Sugeno fuzzy model and adaptive adjustment

In this paper, an approach to the control of continuous-time chaotic systems is proposed using the Takagi--Sugeno (TS) fuzzy model and adaptive adjustment. Sufficient conditions are derived to guarantee chaos control from Lyapunov stability theory. The proposed approach offers a systematic design procedure for stabilizing a large class of chaotic systems in the literature about chaos research. The simulation results on R\"{o}ssler's system verify the effectiveness of the proposed methods.     

Synchronisation of chaotic systems using a novel sampled-data fuzzy controller

This paper presents the synchronisation of chaotic systems using a sampled-data fuzzy controller and is meaningful for many physical real-life applications. Firstly, a Takagi--Sugeno (T--S) fuzzy model is employed to represent the chaotic systems that contain some nonlinear terms, then a type of fuzzy sampled-data controller is proposed and an error system formed by the response and drive chaotic system. Secondly, relaxed LMI-based synchronisation conditions are derived by using a new parameter-dependent Lyapunov--Krasovskii functional and relaxed stabilisation techniques for the underlying error system. The derived LMI-based conditions are used to aid the design of a sampled-data fuzzy controller to achieve the synchronisation of chaotic systems. Finally, a numerical example is provided to illustrate the effectiveness of the proposed results.     

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.