Stability of Fractional Order Fuzzy Cellular Neural Networks with Distributed Delays via Hybrid Feedback Controllers

Neural Processing Letters - This article studies the global Mittag-Leffler stability of fractional order fuzzy cellular neural networks via hybrid feedback controllers. Based on hybrid feedback...     

Global Passivity Analysis of Interval Neural Networks with Discrete and Distributed Delays of Neutral Type

This paper is concerned with delay-dependent passivity analysis for delayed neural networks (DNNs) of neutral type. We first discuss the passivity conditions for DNNs without uncertainties and then extend this result to the case of interval uncertainties. By partitioning the delay intervals into multiple equidistant subintervals, some appropriate Lyapunov-Krasovskii functionals (LKFs) are constructed on these intervals. Considering these new LKFs and using free-weighting matrix approach, several new passivity criteria are proposed in terms of linear matrix inequalities, which are dependent on the size of the time delay. Finally, five numerical examples are given to illustrate the effectiveness and less conservatism of the developed techniques.     

Global Asymptotic Stability of Fuzzy Cellular Neural Networks with Unbounded Distributed Delays

Asymptotic stability problem of a class of fuzzy cellular neural networks with unbounded distributed delays is studied. New stability criteria are derived by employing a Lyapunov-Krasovskii functional and using LMI approach. Numerical examples are provided to illustrate the effectiveness and less conservativeness of the developed techniques.     

Exponential Synchronization of Stochastic Fuzzy Cellular Neural Networks with Reaction-Diffusion Terms via Periodically Intermittent Control

This paper considers the exponential synchronization of stochastic fuzzy cellular neural networks with time-varying delays and reaction-diffusion terms based on p-norm. Motivated by the achievements from both the stability of fuzzy cellular neural networks with stochastic perturbation and reaction-diffusion effects and the synchronization issue of coupled chaotic delayed neural networks by using periodically intermittent control approach, a periodically intermittent controller is proposed to guarantee the exponential synchronization of the coupled chaotic neural networks by using Lyapunov stability theory and stochastic analysis approaches. The synchronization results presented in this paper generalize and improve many known results. This paper also presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed scheme.     

Finite-time Synchronization of Neural Networks with Multiple Proportional Delays via Non-chattering Control

International Journal of Control, Automation and Systems - This paper investigates finite-time synchronization of neural networks (NNs) with multiple proportional delays. In order to cope with the...     

Finite-Time Synchronization of Complex-Valued Neural Networks with Multiple Time-Varying Delays and Infinite Distributed Delays

Neural Processing Letters - This paper investigates finite-time synchronization of complexed-valued neural networks with multiple time-varying delays and infinite distributed delays. By separating...     

Adaptive Synchronization for a Class of Fractional Order Time-delay Uncertain Chaotic Systems via Fuzzy Fractional Order Neural Network

International Journal of Control, Automation and Systems - Uncertainty and delay are common phenomena in chaotic systems, but their existence will increase the difficulty of synchronization. For...     

A Robust Non-Fragile Control Lag Synchronization for Fractional Order Multi-Weighted Complex Dynamic Networks with Coupling Delays

Neural Processing Letters - This problem addresses the fractional order lag synchronization for multi-weighted complex dynamical networks with coupling delays via non-fragile control. Establishing...     

Pinning Exponential Synchronization of Nonlinearly Coupled Neural Networks with Mixed Delays via Intermittent Control

This paper is concerned with the exponential synchronization problem of nonlinearly coupled neural networks with mixed delays. By employing the intermittent control strategy, several appropriate linear and adaptive pinning controllers are designed in each control period. With the help of a new differential inequality, some conditions are proposed to guarantee that the coupled networks can realize pinning synchronization exponentially. The minimum number of pinned nodes is determined by using high-degree pinning scheme. Two numerical examples are provided finally to demonstrate the effectiveness of the theoretical results.     

变时滞随机忆阻器神经网络的同步控制

在实现实际的复杂人工神经网络模型以及大规模集成电路时,随机噪声是不可避免的.因此,随机忆阻器神经网络具有重要的现实研究意义.针对变时滞随机忆阻器神经网络的同步控制问题,基于非光滑分析以及集值映射、随机微分包含的理论,利用Lyapunov函数和基本不等式的方法,设计了一个线性反馈控制器.通过恰当选择控制器增益,实现了随机忆阻器神经网络驱动系统与相应的响应系统之间的指数同步,所得到的结果保守性更小.最后,给出数值例子验证了理论结果的有效性.     

含离散和分布时变时延神经网络系统的指数稳定性

针对一类含有离散和分布时延神经网络,在神经激活函数较弱的约束条件下,通过定义一个更具一般性的Lyapunov泛函,使用凸组合技术,得到了新的基于线性矩阵不等式表示的指数稳定性判据.与现有结果相比,这些判据具有较小的保守性.仿真算例表明,得到的结果是有效的且保守性小.     

Proportional Plus Derivative State Feedback Control of Takagi-Sugeno Fuzzy Singular Fractional Order Systems

This paper investigates the fuzzy normalization and stabilization issues of a class of singular fractional order nonlinear systems with order 0 < α < 1 based on a singular Takagi-Sugeno fuzzy model. First, we present the admissibility theorem of Takagi-Sugeno fuzzy singular fractional order systems. Next, benefited by that the fuzzy model and the state feedback controllers do not share the same membership functions, a proportional plus derivative state feedback controller is designed, which guarantees the closed-loop system normalized and admissible. Finally, a numerical simulation example is given to illustrate the effectiveness of the proposed method.

     

Robust Output Feedback Control for Fractional Order Nonlinear Systems with Time-varying Delays

Robust controller design problem is investigated for a class of fractional order nonlinear systems with time varying delays. Firstly, a reduced-order observer is designed. Then, an output feedback controller is designed. Both the designed observer and controller are independent of time delays. By choosing appropriate Lyapunov functions, we prove the designed controller can render the fractional order system asymptotically stable. A simulation example is given to verify the effectiveness of the proposed approach.      

Synchronization and State Estimation for Discrete-Time Complex Networks With Distributed Delays

In this paper, a synchronization problem is investigated for an array of coupled complex discrete-time networks with the simultaneous presence of both the discrete and distributed time delays. The complex networks addressed which include neural and social networks as special cases are quite general. Rather than the commonly used Lipschitz-type function, a more general sector-like nonlinear function is employed to describe the nonlinearities existing in the network. The distributed infinite time delays in the discrete-time domain are first defined. By utilizing a novel Lyapunov–Krasovskii functional and the Kronecker product, it is shown that the addressed discrete-time complex network with distributed delays is synchronized if certain linear matrix inequalities (LMIs) are feasible. The state estimation problem is then studied for the same complex network, where the purpose is to design a state estimator to estimate the network states through available output measurements such that, for all admissible discrete and distributed delays, the dynamics of the estimation error is guaranteed to be globally asymptotically stable. Again, an LMI approach is developed for the state estimation problem. Two simulation examples are provided to show the usefulness of the proposed global synchronization and state estimation conditions. It is worth pointing out that our main results are valid even if the nominal subsystems within the network are unstable.      

Finite Time Synchronization of Delayed Quaternion Valued Neural Networks with Fractional Order

Neural Processing Letters - In this article, the finite time (FT) synchronization problem of fractional order quaternion valued neural networks with time delay is investigated. Without separating...     

Anti-periodic Synchronization of Quaternion-valued Generalized Cellular Neural Networks with Time-varying Delays and Impulsive Effects

International Journal of Control, Automation and Systems - In this paper, a class of quaternion-valued generalized cellular neural networks (QVGCNNs) with time-varying delays and impulsive effects...     

Exponential Synchronization of Stochastic Memristive Recurrent Neural Networks Under Alternate State Feedback Control

International Journal of Control, Automation and Systems - This paper solves the exponential synchronization problem of two memristive recurrent neural networks with both stochastic disturbance and...     

异时滞分数阶细胞神经网络的电路设计与仿真

针对目前细胞神经网络的电路模型中鲜有人考虑系统延时的问题,提出一种异时滞分数阶细胞神经网络模型.用Matlab数值仿真结果表明理论可行,再借助Multisim软件分别构造输出函数单元电路、分数阶单元电路,用带LCL低通滤波的有源电路在一定频率附近产生固定时滞值τ,研究不同时滞组合下系统的混沌特性.电路仿真结果表明,改变...     

Exponential Stability on Stochastic Neural Networks With Discrete Interval and Distributed Delays

This brief addresses the stability analysis problem for stochastic neural networks (SNNs) with discrete interval and distributed time-varying delays. The interval time-varying delay is assumed to satisfy $0≪d_{1}leq d(t) leq d_{2}$ and is described as $d(t)= d_{1}+h(t)$ with $0leq h(t) leq d_{2}-d_{1}$. Based on the idea of partitioning the lower bound $d_{1}$, new delay-dependent stability criteria are presented by constructing a novel Lyapunov–Krasovskii functional, which can guarantee the new stability conditions to be less conservative than those in the literature. The obtained results are formulated in the form of linear matrix inequalities (LMIs). Numerical examples are provided to illustrate the effectiveness and less conservatism of the developed results.