On the stability of nonautonomous systems

In Kalitine (RAIRO Automatique/Systems Anal. Control 16(3) (1982) 275) the use of semi-definite Lyapunov functions for exploring the local stability of autonomous dynamical systems has been introduced. In this paper, we give an extension of the results of Kalitine (1982) that allows to study the local stability of nonautonomous differential systems. We give an application to the algebraic Riccati equation.     

Improved delay-dependent stability criteria for neural networks with two additive time-varying delay components

In this paper, the problem of stability criteria of neural networks with two additive time-varying delay components is investigated. Some new delay-dependent stability criteria are derived in terms of linear matrix inequalities by choosing a new class of Lyapunov functional. The obtained criteria are less conservative because reciprocally convex approach and convex polyhedron approach are considered. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.     

Almost sure exponential stability of stochastic cellular neural networks with unbounded distributed delays

In this paper, a cellular neural network whose state variables are governed by stochastic non-linear integro-differential equations is investigated. The considered delays are distributed continuously over unbounded intervals. By applying the Lyapunov functional method, the semimartingale convergence theorem, and some inequality technique, we obtain some sufficient criteria to check the almost sure exponential stability of the system, which generalizes and improves some earlier publications. Two examples are also given to demonstrate our results.     

Robust exponential stability of uncertain impulsive neural networks with time-varying delays and delayed impulses

In this paper, the robust exponential stability of uncertain impulsive neural networks with time-varying delays and delayed impulses is considered. It is assumed that the considered impulsive neural networks have norm-bounded parametric uncertainties and time-varying delays and the state variables on the impulses may relate to the time-varying delays. By using Lyapunov functions together with Razumikhin technique or with differential inequalities, some new robust exponential stability criteria are provided. Some examples and their simulations, including examples that the stability of which can not be tackled by the existing results, are also presented to illustrate the effectiveness and the advantage of the obtained results.     

随机时滞神经网络的全局指数稳定性

首先对一般随机系统的渐近特性进行了讨论.然后结合神经网络的特点,应用李雅普诺夫第二方法对一类随机时滞神经网络系统的全局指数稳定性进行了分析,给出了易于判定随机时滞神经网络几乎必然指数稳定性新的代数判据,并给出实例进行仿真实验.     

On the absolute stability approach to quantized feedback control

By exploring some geometric properties of the logarithmic quantizer and using the fact that the logarithmic quantizer is sector bounded and nondecreasing, this paper presents a new approach to the stability analysis of quantized feedback control systems. Our method is based on Tsypkin-type Lyapunov functions that have been widely used in absolute stability analysis problems. The results are expressed in linear matrix inequalities (LMIs) and are valid for both single-input and multiple-input discrete-time linear systems with a logarithmic quantizer. Both theoretical analysis and numerical examples show that the results in this paper are generally less conservative than those in the quadratic framework.     

On asymptotic stability of discrete-time non-autonomous delayed Hopfield neural networks

In this paper, we obtain some sufficient conditions for determining the asymptotic stability of discrete-time non-autonomous delayed Hopfield neural networks by utilizing the Lyapunov functional method. An example is given to show the validity of the results.     

Asymptotic stability of continuous-time systems with saturation nonlinearities

A new criterion is established for global asymptotic stability of second-order systems modeled by equations of the type , where σ is the saturation function. The derivation is based on the Bendixon's theorem on limit cycles and a closer study of the trajectories of the systems. Applications to stabilization of more general cascade nonlinear systems are also discussed.     

具有时滞的脉冲随机神经网络的有限时间稳定性

本文研究具有时滞的脉冲随机神经网络的有限时间稳定性问题.利用Lyapunov泛函技术,线性矩阵不等式(LMIs)工具和平均脉冲区间条件,对反镇定型、中立型和镇定型3种类型的脉冲系统分别给出了基于矩阵不等式的有限时间均方稳定的充分条件,最后通过一个数值例子验证了理论结果的有效性.     

A geometry-based algorithm for the stability of planar switching systems

This paper describes a geometric procedure, whose aim is to provide necessary and sufficient conditions for the stability of a class of planar switching systems against arbitrary switching. The main idea is to build the worst switching sequence and to exploit it to get information about the stability for every switching sequence. The analysis is conveyed initially for LTI subsystems, then it is extended to homogeneous subsystems of the same degree having the origin as the unique equilibrium point. Finally, a simple method to build suitable Lyapunov functions to verify the stability of such systems is provided.     

An adaptive wavelet differential neural networks based identifier and its stability analysis

In this paper, identification problem of a general class of nonlinear dynamic systems is fully considered using adaptive wavelet differential neural networks. In these networks, the activation functions are described by wavelets where parameters are tuned adaptively. The stability analysis of such identifiers is performed by means of Lyapunov analysis. Asymptotic convergence of the error and boundedness of the parameters are proven. To validate the approach, the neuro-identifier is applied to both the Van der pole oscillator and the twin-tanks plant. The simulation results show that the proposed neuro-identifier outperforms the sigmoid based differential neural network identifier.     

时延细胞神经网络的全局渐近稳定性

通过构造新的Lyapunov泛函,在Lyapunov泛函中巧妙引入可调的实参数,并结合不等式运用的一些技巧,讨论了时延细胞神经网络的全局渐近稳定性问题,得到了该模型的平衡点全局渐近稳定的一些新的充分条件。所得的结果改进推广了已有文献中相应的一些结论,并且可应用于以前所不能处理的若干情形。理论分析和数学推导表明,全局渐近稳定性的一个简单充分判据与时延是有关的。所得结果突出了时延对于细胞神经网络的全局渐近稳定性的影响,这对于设计带时延的细胞神经网络有着重要的参考价值。此外,通过实例说明了相应结果的应用,这在理论上和应用中都有着重要的意义。     

Input-output-to-state stability for discrete-time systems

This paper presents Lyapunov characterizations of uniform output-to-state stability and uniform input-output-to-state stability (IOSS) (with respect to disturbances) for discrete-time (DT) nonlinear systems. We show the equivalence of the following three properties for DT systems: uniform IOSS, existence of a smooth Lyapunov function for uniform IOSS, and existence of a (state-)norm estimator. This equivalence result is a DT counterpart of Krichman et al. [2001. Input-output-to-state stability. SIAM Journal on Control and Optimization 39, 1874-1928. Theorem 2.4] and a generalization of Jiang and Wang [2002. A converse Lyapunov theorem for discrete-time systems with disturbances. Systems and Control Letters 45, 49-58. Theorem 1.1] and Jiand and Wang [2001. Input-to-state stability for discrete-time nonlinear systems. Automatica 37, 857-869. Theorem 1, 1⇔4].     

Novel decentralized adaptive strategies for the synchronization of complex networks

This paper is concerned with the analysis of the synchronization of networks of nonlinear oscillators through an innovative local adaptive approach. In particular, time-varying feedback coupling gains are considered, whose gradient is a function of the local synchronization error over each edge in the network. It is shown that, under appropriate conditions, the strategy is indeed successful in guaranteeing the achievement of a common synchronous evolution for all oscillators in the network. The theoretical derivation is complemented by its validation on a set of representative examples.     

Dissipativity and synchronization of fractional-order output-coupled neural networks with multiple adaptive coupling weights

Different from the majority of existing results that focus on the passivity analysis of nonlinear systems, this paper attempts to analyze the more general QSR-dissipativity of fractional-order neural networks that own output coupling and multiple coupled weights, where outer coupling weights can be adjusted online by developing an adaptation law. Using the linear matrix inequality technique, several sufficient criteria that not only guarantee the QSR-dissipativity of the network system but also achieve global synchronization even under zero input are established. Of particular significance is the proposal of a fractional Lyapunov-like lemma, which plays a crucial role in verifying the asymptotic stability of synchronization errors. Finally, a simulation example is presented to verify the plausibility of the theoretical results.     

The almost periodic solution of Lotka-Volterra recurrent neural networks with delays

By the fixed-point theorem subject to different polyhedrons and some inequalities (e.g., the inequality resulted from quadratic programming), we obtain three theorems for the Lotka-Volterra recurrent neural networks having almost periodic coefficients and delays. One of the three theorems can only ensure the existence of an almost periodic solution, whose existence and uniqueness the other two theorems are about. By using Lyapunov function, the sufficient condition guaranteeing the global stability of the solution is presented. Furthermore, two numerical examples are employed to illustrate the feasibility and validity of the obtained criteria. Compared with known results, the networks model is novel, and the results are extended and improved.     

Exponential stability of impulsive discrete-time systems with infinite delays

This paper investigates the exponential stability of impulsive discrete-time systems with infinite delays. Some sufficient conditions are obtained to guarantee the exponential stability of the considered systems by employing Lyapunov functions together with Razumikhin technique. Finally, three numerical examples are given to illustrate the effectiveness and superiority of the obtained results. It is the first time that the exponential stability of impulsive discrete-time systems with infinite delays is studied, the obtained results complement some recent works.     

Feed-back neural networks with discrete weights

We use the Monte Carlo Adaptation learning algorithm to design feed-back neural networks with discrete weights. The dynamic properties of these types of neural networks are investigated as a function of the states of weights. The numerical results of these networks show three phases: the “chaos phase,” the “pure memory phase” and the “mixture phase” in the parameter space. The maximum storage ratio for the “pure memory phase” increases with the increasing of the states of the weights, which is favorable for practical applications.     

Finite-time stability of neutral-type neural networks with random time-varying delays

This paper is devoted to the finite-time stability analysis of neutral-type neural networks with random time-varying delays. The randomly time-varying delays are characterised by Bernoulli stochastic variable. This result can be extended to analysis and design for neutral-type neural networks with random time-varying delays. On the basis of this paper, we constructed suitable Lyapunov–Krasovskii functional together and established a set of sufficient linear matrix inequalities approach to guarantee the finite-time stability of the system concerned. By employing the Jensen's inequality, free-weighting matrix method and Wirtinger's double integral inequality, the proposed conditions are derived and two numerical examples are addressed for the effectiveness of the developed techniques.