共查询到20条相似文献,搜索用时 15 毫秒
1.
Linear matrix inequality approach for robust stability analysis for stochastic neural networks with time-varying delay 下载免费PDF全文
This paper studies the problem of linear matrix inequality (LMI) approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach,the information of the delayed plant states can be taken into full consideration. Based on the new Lyapunov-Krasovskii functional,some inequality techniques and stochastic stability theory,new delay-dependent stability criteria are obtained in terms of LMIs. The proposed results prove the less conservatism,which are realized by choosing new Lyapunov matrices in the decomposed integral intervals. Finally,numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI method. 相似文献
2.
Novel delay-dependent stability criteria for neural networks with interval time-varying delay 下载免费PDF全文
The problem of delay-dependent asymptotic stability for neural networks with interval time-varying delay is investigated. Based on the idea of delay decomposition method, a new type of Lyapunov-Krasovskii functional is constructed. Several novel delay-dependent stability criteria are presented in terms of linear matrix inequality by using the Jensen integral inequality and a new convex combination technique. Numerical examples are given to demonstrate that the proposed method is effective and less conservative. 相似文献
3.
Robust stability analysis of Takagi-Sugeno uncertain stochastic fuzzy recurrent neural networks with mixed time-varying delays 下载免费PDF全文
M. Syed Ali 《中国物理 B》2011,20(8):80201-080201
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. 相似文献
4.
Novel delay dependent stability analysis of Takagi—Sugeno fuzzy uncertain neural networks with time varying delays 下载免费PDF全文
M. Syed Ali 《中国物理 B》2012,21(7):70207-070207
This paper presents the stability analysis for a class of neural networks with time varying delays that are represented by the Takagi-Sugeno (T-S) model. The main results given here focus on the stability criteria using a new Lyapunov functional. New relaxed conditions and new linear matrix inequality-based designs are proposed that outperform the previous results found in the literature. Numerical examples are provided to show that the achieved conditions are less conservative than the existing ones in the literature. 相似文献
5.
Robust stability analysis for Markovian jumping stochastic neural networks with mode-dependent time-varying interval delay and multiplicative noise 下载免费PDF全文
This paper is concerned with the problem of robust stability for a
class of Markovian jumping stochastic neural networks (MJSNNs)
subject to mode-dependent time-varying interval delay and
state-multiplicative noise. Based on the Lyapunov--Krasovskii functional
and a stochastic analysis approach, some new delay-dependent
sufficient conditions are obtained in the linear matrix inequality
(LMI) format such that delayed MJSNNs are globally asymptotically
stable in the mean-square sense for all admissible uncertainties. An
important feature of the results is that the stability criteria are
dependent on not only the lower bound and upper bound of delay for all
modes but also the covariance matrix consisting of the correlation
coefficient. Numerical examples are given to illustrate the
effectiveness. 相似文献
6.
The problem of delay-dependent asymptotic stability criteria for neural networks with time-varying delay is investigated. A new class of Lyapunov functional is constructed to derive some new delay-dependent stability criteria.The obtained criterion are less conservative because free-weighting matrices method and a convex optimization approach are considered. Finally, numerical examples are given to demonstrate the effectiveness of the proposed method. 相似文献
7.
The problem of stability analysis of neural networks with time-varying delay in a given range is investigated in this Letter. By introducing a new Lyapunov functional which uses the information on the lower bound of the delay sufficiently and an augmented Lyapunov functional which contains some triple-integral terms, some improved delay-dependent stability criteria are derived using the free-weighting matrices method. Numerical examples are presented to illustrate the less conservatism of the obtained results and the effectiveness of the proposed method. 相似文献
8.
In this paper, global synchronization of general delayed complex networks with stochastic disturbances, which is a zero-mean real scalar Wiener process, is investigated. The networks under consideration are continuous-time networks with time-varying delay. Based on the stochastic Lyapunov stability theory, It?'s differential rule and the linear matrix inequality (LMI) optimization technique, several delay-dependent synchronous criteria are established, which guarantee the asymptotical mean-square synchronization of drive networks and response networks with stochastic disturbances. The criteria are expressed in terms of LMI, which can be easily solved using the Matlab LMI Control Toolbox. Finally, two examples show the effectiveness and feasibility of the proposed synchronous conditions. 相似文献
9.
Improved delay-dependent globally asymptotic stability of delayed uncertain recurrent neural networks with Markovian jumping parameters 下载免费PDF全文
In this paper, we have improved delay-dependent stability
criteria for recurrent neural networks with a delay varying over a
range and Markovian jumping parameters. The criteria improve over
some previous ones in that they have fewer matrix variables yet less
conservatism. In addition, a numerical example is provided to
illustrate the applicability of the result using the linear matrix
inequality toolbox in MATLAB. 相似文献
10.
Stability analysis of Markovian jumping stochastic Cohen Grossberg neural networks with discrete and distributed time varying delays 下载免费PDF全文
M. Syed Ali 《中国物理 B》2014,(6):131-137
In this paper, the global asymptotic stability problem of Markovian jumping stochastic Cohen-Grossberg neural networks with discrete and distributed time-varying delays (MJSCGNNs) is considered. A novel LMI-based stability criterion is obtained by constructing a new Lyapunov functional to guarantee the asymptotic stability of MJSCGNNs. Our results can be easily verified and they are also less restrictive than previously known criteria and can be applied to Cohen-Grossberg neural networks, recurrent neural networks, and cellular neural networks. Finally, the proposed stability conditions are demonstrated with numerical examples. 相似文献
11.
Some criteria for the global stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters are presented. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. By employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish some easy-to-test criteria of global exponential stability in the mean square for the stochastic neural networks. The criteria are computationally efficient, since they are in the forms of some linear matrix inequalities. 相似文献
12.
Nonlinear H∞ control of structured uncertain stochastic neural networks with discrete and distributed time varying delays 下载免费PDF全文
This paper is concerned with the problem of robust H∞ control for structured uncertain stochastic neural networks with both discrete and distributed time varying delays. A sufficient condition is presented for the existence of H∞ control based on the Lyapunov stability theory. The stability criterion is described in terms of linear matrix inequalities (LMIs), which can be easily checked in practice. An example is provided to demonstrate the effectiveness of the proposed result. 相似文献
13.
Novel stability criteria for fuzzy Hopfield neural networks based on an improved homogeneous matrix polynomials technique 下载免费PDF全文
The global stability problem of Takagi-Sugeno(T-S) fuzzy Hopfield neural networks(FHNNs) with time delays is investigated.Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism.Firstly,using both Finsler’s lemma and an improved homogeneous matrix polynomial technique,and applying an affine parameter-dependent Lyapunov-Krasovskii functional,we obtain the convergent LMI-based stability criteria.Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique.Secondly,to further reduce the conservatism,a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs,which is suitable to the homogeneous matrix polynomials setting.Finally,two illustrative examples are given to show the efficiency of the proposed approaches. 相似文献
14.
Exponential stability of cellular neural networks with multiple time delays and impulsive effects 下载免费PDF全文
In this work, the stability issues of the equilibrium points of the cellular neural networks with multiple time delays and impulsive effects are investigated. Based on the stability theory of Lyapunov-Krasovskii, the method of linear matrix inequality (LMI) and parametrized first-order model transformation, several novel conditions guaranteeing the delaydependent and the delay-independent exponential stabilities are obtained. A numerical example is given to illustrate the effectiveness of our results. 相似文献
15.
This Letter concerns with the mean square exponential stability of uncertain stochastic delayed neural networks. By applying Lyapunov functional method, new delay-dependent/independent mean square exponential stability criteria are derived in terms of linear matrix inequalities. Two examples are presented which show our result are less conservative than the existing stability criteria. 相似文献
16.
This paper deals with the issue of synchronization of delayed complex networks. Differing from previous results,the delay interval [0,d(t)] is divided into some variable subintervals by employing a new method of weighting delays. Thus,new synchronization criteria for complex networks with time-varying delays are derived by applying this weighting-delay method and introducing some free weighting matrices. The obtained results have proved to be less conservative than previous results. The sufficient conditions of asymptotical synchronization are derived in the form of linear matrix inequality,which are easy to verify. Finally,several simulation examples are provided to show the effectiveness of the proposed results. 相似文献
17.
This paper studies delay-dependent asymptotical stability
problems for the neural system with time-varying delay. By dividing the
whole interval into multiple segments such that each segment has a
different Lyapunov matrix, some improved delay-dependent stability
conditions are derived by employing an integral equality technique. A
numerical example is given to demonstrate the effectiveness and
less conservativeness of the proposed methods. 相似文献
18.
19.
O.M. Kwon 《Physics letters. A》2010,374(10):1232-5781
This Letter investigates the problem of delay-dependent exponential stability analysis for uncertain stochastic neural networks with time-varying delay. Based on the Lyapunov stability theory, improved delay-dependent exponential stability criteria for the networks are established in terms of linear matrix inequalities (LMIs). 相似文献