共查询到20条相似文献,搜索用时 15 毫秒
1.
In this Letter, we investigate the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters as well as mode-dependent mixed time-delays. The parameters of the discrete-time neural networks are subject to the switching from one mode to another at different times according to a Markov chain, and the mixed time-delays consist of both discrete and distributed delays that are dependent on the Markovian jumping mode. New techniques are developed to deal with the mixed time-delays in the discrete-time setting, and a novel Lyapunov-Krasovskii functional is put forward to reflect the mode-dependent time-delays. Sufficient conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A numerical example is exploited to show the usefulness of the derived LMI-based conditions. 相似文献
2.
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
3.
Stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks with mixed delays and the Wiener process based on sampled-data control 下载免费PDF全文
We investigate the stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks (MJFCNNs) with discrete, unbounded distributed delays, and the Wiener process based on sampled-data control using the linear matrix inequality (LMI) approach. The Lyapunov-Krasovskii functional combined with the input delay approach as well as the free-weighting matrix approach is employed to derive several sufficient criteria in terms of LMIs to ensure that the delayed MJFCNNs with the Wiener process is stochastic asymptotical synchronous. Restrictions (e.g., time derivative is smaller than one) are removed to obtain a proposed sampled-data controller. Finally, a numerical example is provided to demonstrate the reliability of the derived results. 相似文献
4.
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
5.
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
6.
This paper proposes new delay-dependent synchronization criteria for coupled Hopfield neural networks with time-varying delays. By construction of a suitable Lyapunov-Krasovskii's functional and use of Finsler's lemma, novel synchronization criteria for the networks are established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. Two numerical examples are given to illustrate the effectiveness of the proposed methods. 相似文献
7.
Exponential synchronization of complex dynamical networks with Markovian jumping parameters using sampled-data and mode-dependent probabilistic time-varying delays 下载免费PDF全文
In this paper, the problem of exponential synchronization of complex dynamical networks with Markovian jumping parameters using sampled-data and Mode-dependent probabilistic time-varying coupling delays is investigated. The sam- pling period is assumed to be time-varying and bounded. The information of probability distribution of the time-varying delay is considered and transformed into parameter matrices of the transferred complex dynamical network model. Based on the condition, the design method of the desired sampled data controller is proposed. By constructing a new Lyapunov functional with triple integral terms, delay-distribution-dependent exponential synchronization criteria are derived in the form of linear matrix inequalities. Finally, two numerical examples are given to illustrate the effectiveness of the proposed methods. 相似文献
8.
This Letter is concerned with the robust state estimation problem for uncertain time-delay Markovian jumping genetic regulatory networks (GRNs) with SUM logic, where the uncertainties enter into both the network parameters and the mode transition rate. The nonlinear functions describing the feedback regulation are assumed to satisfy the sector-like conditions. The main purpose of the problem addressed is to design a linear estimator to approximate the true concentrations of the mRNA and protein through available measurement outputs. By resorting to the Lyapunov functional method and some stochastic analysis tools, it is shown that if a set of linear matrix inequalities (LMIs) is feasible, the desired state estimator, that can ensure the estimation error dynamics to be globally robustly asymptotically stable in the mean square, exists. The obtained LMI conditions are dependent on both the lower and the upper bounds of the delays. An illustrative example is presented to demonstrate the feasibility of the proposed estimation schemes. 相似文献
9.
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
10.
In this paper,the problem of stability analysis for neural networks with time-varying delays is considered.By constructing a new augmented Lyapunov-Krasovskii’s functional and some novel analysis techniques,improved delaydependent criteria for checking the stability of the neural networks are established.The proposed criteria are presented in terms of linear matrix inequalities(LMIs) which can be easily solved and checked by various convex optimization algorithms.Two numerical examples are included to show the superiority of our results. 相似文献
11.
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
12.
Yan Liu 《Physics letters. A》2009,373(41):3741-3742
In a previous work [Z.D. Wang, Y.R. Liu, L. Yu, X.H. Liu, Phys. Lett. A 356 (2006) 346] an exponential stability analysis for a class of Markovian jumping neural networks (MJNNs) was presented. In this Letter we employ the same technique to extend the results for MJNNs with time-varying delays and mode estimation, appropriate for active fault-tolerant control systems. 相似文献
13.
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
14.
In this Letter, we investigate the exponential synchronization problem for an array of N linearly coupled complex networks with Markovian jump and mixed time-delays. The complex network consists of m modes and the network switches from one mode to another according to a Markovian chain with known transition probability. The mixed time-delays are composed of discrete and distributed delays, both of which are mode-dependent. The nonlinearities imbedded with the complex networks are assumed to satisfy the sector condition that is more general than the commonly used Lipschitz condition. By making use of the Kronecker product and the stochastic analysis tool, we propose a novel Lyapunov–Krasovskii functional suitable for handling distributed delays and then show that the addressed synchronization problem is solvable if a set of linear matrix inequalities (LMIs) are feasible. Therefore, a unified LMI approach is developed to establish sufficient conditions for the coupled complex network to be globally exponentially synchronized in the mean square. Note that the LMIs can be easily solved by using the Matlab LMI toolbox and no tuning of parameters is required. A simulation example is provided to demonstrate the usefulness of the main results obtained. 相似文献
15.
Synchronization of stochastically hybrid coupled neural networks with coupling discrete and distributed time-varying delays 下载免费PDF全文
A general model of linearly stochastically coupled identical connected neural networks with hybrid coupling is proposed, which is composed of constant coupling, coupling discrete time-varying delay and coupling distributed timevarying delay. All the coupling terms are subjected to stochastic disturbances described in terms of Brownian motion, which reflects a more realistic dynamical behaviour of coupled systems in practice. Based on a simple adaptive feedback controller and stochastic stability theory, several sufficient criteria are presented to ensure the synchronization of linearly stochastically coupled complex networks with coupling mixed time-varying delays. Finally, numerical simulations illustrated by scale-free complex networks verify the effectiveness of the proposed controllers. 相似文献
16.
Global asymptotic stability of fuzzy cellular neural networks with time-varying delays 总被引:1,自引:0,他引:1
In this Letter fuzzy cellular neural networks with time-varying delays are studied. Sufficient conditions for the existence, uniqueness and global asymptotic stability of equilibrium point are established by using the theory of topological degree and applying the properties of nonsingular M-matrix. The activation functions are not required to be differentiable, bounded or monotone nondecreasing. The results of this Letter are new and they complement previously known results. 相似文献
17.
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
18.
19.
《Chinese Journal of Physics (Taipei)》2018,56(5):2448-2464
The objective of this paper is to analyze the finite time problem of a class of neutral-type Markovian jump neural networks with time varying delays and parametric uncertainties using decentralized event-triggered communication scheme. We present a methodology for designing decentralized event-triggered, which utilize only locally available information, for determining the time instants of transmission from the sensors to the central controller. Based on the Lyapunov function with inequality techniques like reciprocal convex combination method, some sufficient conditions are derived to guarantee the finite-time stability of the considered neural networks. Furthermore, the decentralized event-triggered scheme combined with state feedback controller and is designed to solve the finite time stability. The obtained stability criteria are stated in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. Finally, numerical examples are given to illustrate the effectiveness and reduced conservatism of the proposed results over the existing ones. 相似文献
20.
Global exponential stability of reaction diffusion neural networks with discrete and distributed time-varying delays 下载免费PDF全文
This paper investigates the global exponential stability of reaction-diffusion neural networks with discrete and distributed time-varying delays.By constructing a more general type of Lyapunov-Krasovskii functional combined with a free-weighting matrix approach and analysis techniques,delay-dependent exponential stability criteria are derived in the form of linear matrix inequalities.The obtained results are dependent on the size of the time-varying delays and the measure of the space,which are usually less conservative than delay-independent and space-independent ones.These results are easy to check,and improve upon the existing stability results.Some remarks are given to show the advantages of the obtained results over the previous results.A numerical example has been presented to show the usefulness of the derived linear matrix inequality(LMI)-based stability conditions. 相似文献