Exponential stability and existence of periodic solutions for a class of recurrent neural networks with delays

Both the global exponential stability and the existence of periodic solutions for a class of recurrent neural networks with continuously distributed delays （RNNs） are studied. By employing the inequality α∏k=1^m bk^qk≤1/r ∑qkbk^r＋1/rα^r（α≥0,bk≥0,qk〉0,with ∑k=1^m qk=r-1,r≥1, constructing suitable Lyapunov r k=l k=l functions and applying the homeomorphism theory, a family of simple and new sufficient conditions are given ensuring the global exponential stability and the existence of periodic solutions of RNNs. The results extend and improve the results of earlier publications.

New Robust Exponential Stability Analysis for Uncertain Neural Networks with Time-varying Delay

In this paper,the global robust exponential stability is considered for a class of neural networks with parametric uncer- tainties and time-varying delay.By using Lyapunov functional method,and by resorting to the new technique for estimating the upper bound of the derivative of the Lyapunov functional,some less conservative exponential stability criteria are derived in terms of linear matrix inequalities (LMIs).Numerical examples are presented to show the effectiveness of the proposed method.

Global exponential stability of switched systems

This paper proposes a method for the stability analysis of deterministic switched systems.Two motivational examples are introduced （nonholonomic system and constrained pendulum）.The finite collection of models consists of nonlinear models,and a switching sequence is arbitrary.It is supposed that there is no jump in the state at switching instants,and there is no Zeno behavior,i.e.,there is a finite number of switches on every bounded interval.For the analysis of deterministic switched systems,the multiple Lyapunov functions are used,and the global exponential stability is proved.The exponentially stable equilibrium of systems is relevant for practice because such systems are robust to perturbations.

Global exponential stability of mixed discrete and distributively delayed cellular neural network

This paper concernes analysis for the global exponential stability of a class of recurrent neural networks with mixed discrete and distributed delays. It first proves the existence and uniqueness of the balance point, then by employing the Lyapunov-Krasovskii functional and Young inequality, it gives the sufficient condition of global exponential stability of cellular neural network with mixed discrete and distributed delays, in addition, the example is provided to illustrate the applicability of the result.

Robust exponential stability and stabilization of linear uncertain polytopic time-delay systems

This paper proposes new sufficient conditions for the exponential stability and stabilization.of linear uncertain polytopic time-delay systems. The conditions for exponential stability are expressed in terms of Kharitonov-type linear matrix inequalities （LMIs） and we develop control design methods based on LMIs for solving stabilization problem. Our method consists of a combination of the LMI approach and the use of parameter-dependent Lyapunov functionals, which allows to compute simultaneously the two bounds that characterize the exponetial stability rate of the solution. Numerical examples illustrating the conditions are given.

Exponential Stability of Periodic Solution for Delayed Hopfield Networks

The paper is devoted to periodic attractor of delayed Hopfield neural networks with time-varying. By constructing Lyapunov functionals and using inequality techniques, some new sufficient criteria are obtained to guarantee the existence and global exponential stability of periodic attractor. Our results improve and extend some existing ones in [13-14]. One example is also worked out to demonstrate the advantages of our results.

Exponential stability and periodic solution for fuzzy BAM Neural networks with time varying delays

In this paper, a class of fuzzy BAM neural networks with time varying delays is discussed. By using the properties of M-matrix, Linear Matrix Inequality（LMI） approach and general Lyapunov-Krasovskii functional, some new sufficient conditions are derived to ensure the existence of periodic solutions and the global exponential stability of the fuzzy BAM neural networks with time varying delays. These results have important significance in the design of global exponential stable BAM networks with delays. Moreover, an example is given to illustrate that the conditions of the results in the paper are feasible.

Global exponential stability analysis of cellular neural networks with multiple time delays

Global exponential stability problems are investigated for cellular neural networks （CNN） with multiple time-varying delays. Several new criteria in linear matrix inequality form or in algebraic form are presented to ascertain the uniqueness and global exponential stability of the equilibrium point for CNN with multiple time-varying delays and with constant time delays. The proposed method has the advantage of considering the difference of neuronal excitatory and inhibitory effects, which is also computationally efficient as it can be solved numerically using the recently developed interior-point algorithm or be checked using simple algebraic calculation. In addition, the proposed results generalize and improve upon some previous works. Two numerical examples are used to show the effectiveness of the obtained results.

GLOBAL EXPONENTIAL STABILITY IN HOPFIELD AND BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH TIME DELAYS

Without assuming the boundedness, strict monotonicity and differentiability of the activation functions, the authors utilize the Lyapunov functional method to analyze the global convergence of some delayed models. For the Hopfield neural network with time delays, a new sufficient condition ensuring the existence, uniqueness and global exponential stability of the equilibrium point is derived. This criterion concerning the signs of entries in the connection matrix imposes constraints on the feedback matrix independently of the delay parameters. From a new viewpoint, the bidirectional associative memory neural network with time delays is investigated and a new global exponential stability result is given.

GLOBAL EXPONENTIAL STABILITY TO A CLASS OF DIFFERENTIAL SYSTEM WITH DELAY

The global exponential stability of the zero solution to a class of differential system with delay is considered. By constructing a suitable type of Lyapunov functional and using some analytical techniques, we derive some criteria to check exponential stability of this system. The results establish a relation between the delay time and the parameters of the system. Two examples are also given to illustrate the validity of the results.

Necessary exponential stability conditions for linear discrete time-delay systems and application

Necessary conditions for the exponential stability of the linear discrete time-delay systems are presented by employing the so-called Lyapunov–Krasovskii functional approach.These conditions not only provide a new tool for stability analysis of the linear discrete timedelay system by characterising instability domains,but also extend the existing results of the linear discrete time-delay system.Simultaneously,we investigate several crucial properties that connect the Lyapunov matrix and the fundamental matrix of the system.Finally,the robust stability analysis of the linear discrete time-delay systems with norm-bounded uncertainties is presented.Numerical examples illustrate the validity of the obtained results.

EXPONENTIAL STABILITY OF DISCRETE-TIME CELLULAR NEURAL NETWORKS WITH DELAYS

In this paper, a new sufficient condition for the global exponential stability of a unique equilibrium point of discrete-time cellular neural networks is given. It is shown that the condition relies on the feedback matrices and is independent of the delay parameter. Furthermore, this condition is less restrictive than those given in the literature.

NEW CRITERIA OF EXPONENTIAL STABILITY FOR BAM NEURAL NETWORKS WITH DELAYS

In this Letter, a novel Lyapunov functional is constructed to investigate the exponential stability of the BAM neural networks. New sufficient conditions of the uniqueness and global exponential stability for the equilibrium of BAM neural networks with delays are obtained. The results improve those existing ones.

EXPONENTIAL STABILITY TO A NEUTRAL DIFFERENTIAL EQUATION OF FIRST ORDER WITH DELAY

In this paper, we study the exponential stability of the zero solution to a neutral diferential equation. By applying the Lyapunov-Krasovskiì functional approach, we prove a result on the stability of the zero solution. The result we obtained extends and generalizes the existing ones in the previous literature. Comparing with the previous results, our result is new and complements some known results.

Exponential stability and periodic solutions of delayed cellular neural networks

A set of criteria are presented for the global exponential stability and the existence of periodic solutions of delayed cellular neural networks (DCNNs) by constructing suitable Lyapunov functionals, introducing many parametersq ij * ,r ij * ,q ij ,r ij ∈R andW i >0 (i, j=1, 2,…,n) and combining them with the elementary inequality 2ab≤a 2+b 2 technique. These criteria have important significance in the design and applications of globally stable DCNNs and periodic oscillatory DCNNs. In addition, the results in literature are extended and improved. Two examples are given to illustrate the theory.

Exponential stabilization of distributed parameter switched systems under dwell time constraints

The exponential stabilization problem for finite dimensional switched systems is extended to the infinite dimensional distributed parameter systems in the Hilbert space. Based on the semigroup theory, by applying the multiple Lyapunov function method, the exponential stabilization conditions are derived. These conditions are given in the form of linear operator inequalities where the decision variables are operators in the Hilbert space; while the stabilization properties depend on the switching rule. Being applied to the two-dimensional heat switched propagation equations with the Dirichlet boundary conditions, these linear operator inequalities are transformed into standard linear matrix inequalities. Finally, two examples are given to illustrate the effectiveness of the proposed results.

Exponential Stabilization of Euler-Bernoulli Beam with Input Time-Delay in the Boundary Control

The boundary control problem of a cantilever Euler-Bernoulli beam with input time delay is considered.In order to exponentially stabilize the system, a feedback controller is adopted.And we study the well-posedness and exponential stability of the closed-loop system.The approach used in this paper is done by several steps.Firstly, the well-posedness of this system is proved by semi-group theory.Secondly, the asymptotical expression of eigenvalue is investigated by spectral analysis.Thirdly, the exponential stability of the system is studied by multiplier technology.Finally, numerical simulations on the dynamical behavior of the system are given to support the results obtained.

Exponential stabilization of networked control systems and design of switching controller

This paper deals with the problem of switching between an open-loop estimator and a close-loop estimator for compensating transmission error and packet dropout of networked control systems. Switching impulse is considered in order to reduce the error between theory and application, a sufficient condition for exponential stabilization of networked control systems under a given switching rule is presented by multiple Lyapunov-like functions. These results are presented for both continuous-time and discrete-time domains. Controllers are designed by means of linear matrix inequalities. Sim- ulation results show the feasibility and efficiency of the proposed method.

Stability of a Class of Neural Networks with Asymmetric Connection Weights

This paper derives some sufficient conditions for exponential stability for the equilibrium point by dividing the state variables of the system according to the characters of the neural networks. The new conditions are described by some blocks of the interconnection matrix. An example is given to demonstrate the effectiveness of the proposed theory.

切换线性随机系统的指数稳定分析:多Lyapunov函数方法

考虑随机扰动对于切换系统稳定性的影响。我们推广多Lyapunov函数方法并利用噪声的统计性质建立几乎必然指数稳定的条件。进而,给出了子系统反馈控制器的设计条件以使闭环系统是几乎必然指数稳定的。最后,通过一个仿真算例验证了方法的有效性。