一类不确定延迟神经网络的自适应投影同步

研究了一类参数不确定的延迟神经网络的投影同步,基于Lyapunov稳定性理论,设计了一种新型的自适应控制方法.该方法能同时实现一类参数不确定延迟神经网络的参数识别和投影同步.数值模拟证明了该方法的有效性. 关键词： 延迟神经网络 Lyapunov稳定性理论 参数识别 投影同步     

基于神经网络的混沌系统鲁棒自适应同步

基于神经网络,研究了两个混沌系统都存在扰动情况下的同步问题.该方法能有效地克服不确定性对同步所造成的破坏,实现了良好的同步效果.针对Lorenz系统进行了数值计算.数值计算结果证明了所给方法的有效性. 关键词： 混沌系统 同步 神经网络     

一类参数不确定混沌系统的延迟同步

针对一类混沌系统，研究了参数未知的混沌系统的延迟同步.基于Lyapunov稳定性定理，给出了延迟同步控制器和参数自适应律的解析表达式.该方法简单、适用范围广.以新混沌系统为例，数值模拟说明了该方法的有效性和可行性.通过研究有界噪声作用下该系统的控制效果，表明了该方法具有较强的鲁棒性和抗干扰能力. 关键词： 延迟同步 Lyapunov稳定性定理 新混沌系统 有界噪声     

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     

混沌时滞神经网络系统的反同步

利用状态观测器方法研究了一类带时滞的混沌神经网络系统的反同步问题.与应用于其他混沌系统的反同步方法相比,提出的方法更为简便,并且利用极点配置技术,只要通过调整特征值来实现反同步速率的快慢.最后,给出了数值例子和计算机仿真结果来验证该方案的有效性. 关键词： 混沌神经网络 状态观测器 极点配置技术 反同步     

环形加权网络的时空混沌延迟同步

研究了环形加权网络的时空混沌延迟同步问题.以随时间和空间演化均呈现混沌行为的时空混沌系统作为网络的节点,通过环形加权连接使所有节点建立关联.基于线性稳定性定理,通过确定网络的最大Lyapunov指数,得到了实现网络延迟同步的条件.在最大Lyapunov指数小于零的区域内,任取节点之间耦合强度的权重值,均可以使整个网络实现延迟同步.采用具有时空混沌行为的自催化反应扩散系统作为网络节点,仿真模拟验证了该方法的有效性. 关键词： 延迟同步 加权网络 时空混沌 Lyapunov指数     

Sprott系统的恒Lyapunov指数谱混沌锁定及其反同步

Sprott系统通过单绝对值项的非线性作用实现混沌,引入新的控制参数,可实现对该系统的恒Lyapunov指数谱混沌锁定,锁定以后的混沌信号幅度与相位可以调节. 基于Lyapunov势函数法,构建了反同步系统,在响应系统中只引入一个控制项,实现了锁定系统的反同步. 关键词： Sprott系统 Lyapunov指数谱 混沌锁定 反同步     

超混沌系统的广义同步化

研究了超混沌系统的广义同步化问题.基于Lyapunov稳定性理论，提出了一种新的线性及非线性广义同步方案，并从理论上证明了该方案的可行性.该方案不仅可以实现相同维数超混沌系统之间的广义同步，而且对于不同维数的系统之间的广义同步问题同样适用.合理地构造误差增益矩阵，即可实现驱动系统和响应系统之间的广义同步.数值模拟实验进一步验证了所提方案的有效性.     

不同结构混沌系统的自适应同步和反同步

针对不同结构混沌系统的同步与反同步问题进行了研究.在系统参数已知时,采用主动控制法实现混沌系统的同步与反同步,并将主动控制器的设计方法进行了推广.在参数未知时,基于Lyapunov稳定性理论和自适应控制方法,给出了自适应控制器和参数自适应律,实现了参数均未知且结构不同的驱动系统和响应系统的同步与反同步.在控制器的设计过程中,将驱动系统和响应系统进行互换,讨论了互换前后的控制器和自适应律之间的关系.数值仿真结果说明了所提出设计方法的有效性. 关键词： 混沌同步 反同步 主动控制法 自适应控制法     

延迟混沌系统投影同步及状态切换的电路实验

利用有源低通滤波器设计了延迟混沌电路,利用低通滤波器的相频特性完成对混沌信号的延时,实现了单向耦合延迟混沌电路的投影同步及不同状态之间的切换.     

Finite-Time Stability of Fractional-Order Neural Networks with Delay

Finite-time stability of a class of fractional-order neural networks is investigated in this paper.By Laplace transform,the generalized Gronwall inequality and estimates of Mittag-Leffler functions,sufficient conditions are presented to ensure the finite-time stability of such neural models with the Caputo fractional derivatives.Furthermore,results about asymptotical stability of fractional-order neural models are also obtained.     

Finite-Time Stability of Fractional-Order Neural Networks with Delay

Finite-time stability of a class of fractional-order neural networks is investigated in this paper. By Laplace transform, the generalized Gronwall inequality and estimates of Mittag-Leffler functions, sufficient conditions are presented to ensure the finite-time stability of such neural models with the Caputo fractional derivatives. Furthermore, results about asymptotical stability of fractional-order neural models are also obtained.     

Control of Unknown Chaotic Systems Based on Neural Predictive Control

We introduce the predictive control into the control of chaotic system and propose a neural networkcontrol algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknownchaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is muchhigher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of thecontrol system and prove the convergence property of the neural controller. The theoretic derivation and simulationsdemonstrate the effectiveness of the algorithm.     

Control of Unknown Chaotic Systems Based on Neural Predictive Control

We introduce the predictive control into the control of chaotic system and propose a neural network control algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is much higher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of the control system and prove the convergence property of the neural controller. The theoretic derivation and simulations demonstrate the effectiveness of the algorithm.     

耦合含时滞的相互依存网络的局部自适应异质同步

针对由两个子网络构成的耦合含时滞的相互依存网络,研究其局部自适应异质同步问题.时滞同时存在于两个子网络的内部耦合项和子网络间的一对一相互依赖耦合项中,且网络的耦合关系满足非线性特性和光滑性.基于李雅普诺夫稳定性理论、线性矩阵不等式方法和自适应控制技术,通过对子网络设置合适的控制器,提出了使得相互依存网络的子网络分别同步到异质孤立系统的充分条件.针对小世界网络和无标度网络构成的相互依存网络进行数值模拟,验证了提出理论的正确性和有效性.     

Winsorization for Robust Bayesian Neural Networks

With the advent of big data and the popularity of black-box deep learning methods, it is imperative to address the robustness of neural networks to noise and outliers. We propose the use of Winsorization to recover model performances when the data may have outliers and other aberrant observations. We provide a comparative analysis of several probabilistic artificial intelligence and machine learning techniques for supervised learning case studies. Broadly, Winsorization is a versatile technique for accounting for outliers in data. However, different probabilistic machine learning techniques have different levels of efficiency when used on outlier-prone data, with or without Winsorization. We notice that Gaussian processes are extremely vulnerable to outliers, while deep learning techniques in general are more robust.     

Robust stability analysis of Takagi-Sugeno uncertain stochastic fuzzy recurrent neural networks with mixed time-varying delays

In this paper,the global stability of Takagi-Sugeno (TS) uncertain stochastic fuzzy recurrent neural networks with discrete and distributed time-varying delays (TSUSFRNNs) is considered.A novel LMI-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of TSUSFRNNs.The proposed stability conditions are demonstrated through numerical examples.Furthermore,the supplementary requirement that the time derivative of time-varying delays must be smaller than one is removed.Comparison results are demonstrated to show that the proposed method is more able to guarantee the widest stability region than the other methods available in the existing literature.     

Robust Stabilization and Synchronization of a Novel Chaotic System with Input Saturation Constraints

In this paper, the robust stabilization and synchronization of a novel chaotic system are presented. First, a novel chaotic system is presented in which this system is realized by implementing a sigmoidal function to generate the chaotic behavior of this analyzed system. A bifurcation analysis is provided in which by varying three parameters of this chaotic system, the respective bifurcations plots are generated and evinced to analyze and verify when this system is in the stability region or in a chaotic regimen. Then, a robust controller is designed to drive the system variables from the chaotic regimen to stability so that these variables reach the equilibrium point in finite time. The robust controller is obtained by selecting an appropriate robust control Lyapunov function to obtain the resulting control law. For synchronization purposes, the novel chaotic system designed in this study is used as a drive and response system, considering that the error variable is implemented in a robust control Lyapunov function to drive this error variable to zero in finite time. In the control law design for stabilization and synchronization purposes, an extra state is provided to ensure that the saturated input sector condition must be mathematically tractable. A numerical experiment and simulation results are evinced, along with the respective discussion and conclusion.