Markovian Jumping Neural Networks Research Articles

Passivity Analysis of Markovian Jumping Neural Networks with Leakage Time-Varying Delays

This paper is concerned with the passivity analysis of Markovian jumping neural networks with leakage time-varying delays. Based on a Lyapunov functional that accounts for the mixed time delays, a leakage delay-dependent passivity conditions are derived in terms of linear matrix inequalities (LMIs). The mixed delays includes leakage time-varying delays, discrete time-varying delays, and distributed time-varying delays. By employing a novel Lyapunov-Krasovskii functional having triple-integral terms, new passivity leakage delay-dependent criteria are established to guarantee the passivity performance. This performance not only depends on the upper bound of the time-varying leakage delay but also depends on the upper bound of the derivative of the time-varying leakage delay . While estimating the upper bound of derivative of the Lyapunov-Krasovskii functional, the discrete and distributed delays should be treated so as to appropriately develop less conservative results. Two numerical examples are given to show the validity and potential of the developed criteria.

Design of sampled data state estimator for Markovian jumping neural networks with leakage time-varying delays and discontinuous Lyapunov functional approach

This paper first points out some fatal errors in the paper (Nonlinear Dyn 73:1367–1383, 2013). Then the main results of the above paper are corrected with some suitable modifications. The new feedback gain matrix is used to prove the estimation of the neuron states through numerical simulations with the same examples as discussed in the above paper.

LMI Approach to Exponential Stability and Almost Sure Exponential Stability for Stochastic Fuzzy Markovian-Jumping Cohen-Grossberg Neural Networks with Nonlinearp-Laplace Diffusion

The robust exponential stability of delayed fuzzy Markovian-jumping Cohen-Grossberg neural networks (CGNNs) with nonlinearp-Laplace diffusion is studied. Fuzzy mathematical model brings a great difficulty in setting up LMI criteria for the stability, and stochastic functional differential equations model with nonlinear diffusion makes it harder. To study the stability of fuzzy CGNNs with diffusion, we have to construct a Lyapunov-Krasovskii functional in non-matrix form. But stochastic mathematical formulae are always described in matrix forms. By way of some variational methods inW1,p(Ω),Itôformula, Dynkin formula, the semi-martingale convergence theorem, Schur Complement Theorem, and LMI technique, the LMI-based criteria on the robust exponential stability and almost sure exponential robust stability are finally obtained, the feasibility of which can efficiently be computed and confirmed by computer MatLab LMI toolbox. It is worth mentioning that even corollaries of the main results of this paper improve some recent related existing results. Moreover, some numerical examples are presented to illustrate the effectiveness and less conservatism of the proposed method due to the significant improvement in the allowable upper bounds of time delays.

Stability Analysis of Stochastic Markovian Jump Neural Networks with Different Time Scales and Randomly Occurred Nonlinearities Based on Delay-Partitioning Projection Approach

In this paper, the mean square asymptotic stability of stochastic Markovian jump neural networks with different time scales and randomly occurred nonlinearities is investigated. In terms of linear matrix inequality (LMI) approach and delay-partitioning projection technique, delay-dependent stability criteria are derived for the considered neural networks for cases with or without the information of the delay rates via new Lyapunov-Krasovskii functionals. We also obtain that the thinner the delay is partitioned, the more obviously the conservatism can be reduced. An example with simulation results is given to show the effectiveness of the proposed approach.

Finite-time boundedness of uncertain time-delayed neural network with Markovian jumping parameters

The stochastic finite-time boundedness (FTB) problem is considered for a class of Markovian jumping neural networks (MJNNs) with time delay and uncertainties. By selecting the appropriate stochastic Lyapunov–Krasovskii functional, sufficient conditions of stochastic FTB of MJNNs are presented and proved. The FTB criteria are formulated in the form of linear matrix inequalities. Simulation results illustrate the effectiveness of the developed approaches.

Exponential state estimation for Markovian jumping neural networks with time-varying discrete and distributed delays

This paper is concerned with the exponential state estimation for Markovian jumping neural networks with time-varying discrete and distributed delays. The parameters of the neural networks are subject to the switching from one mode to another according to a Markov chain. By constructing a novel Lyapunov-Krasovskii functional and developing a new convex combination technique, a new delay-dependent exponential stability condition is proposed, such that for all admissible delay bounds, the resulting estimation error system is mean-square exponentially stable with a prescribed noise attenuation level in the H(∞) sense. It is also shown that the design of the desired state estimator is achieved by solving a set of linear matrix inequalities (LMIs). The obtained condition implicitly establishes the relations among the maximum delay bounds, H(∞) noise attenuation level and the exponential decay rate of the estimation error system. Finally, a numerical example is given to show the effectiveness of the proposed result.

Delay-dependent stochastic stability criteria for Markovian jumping neural networks with mode-dependent time-varying delays and partially known transition rates

In this paper, the problem of stochastic stability criterion of Markovian jumping neural networks with mode-dependent time-varying delays and partially known transition rates is considered. Some new delay-dependent stability criteria are derived by choosing a new class of Lyapunov functional. The obtained criteria are less conservative because free-weighting matrices method and a convex optimization approach are considered. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

New results on exponential stability for Markovian jumping neural networks with time-varying discrete and distributed delays

The present paper is concerned with the mean-square exponential stability for a class of Markovian jumping neural networks with time-varying discrete and distributed delays. The discrete delay is assumed to be time-varying and belong to a given interval, where the lower and upper bounds of delay are available. Based on the newly constructed Lyapunov—Krasovskii functional, some new delay-interval-dependent exponential stability criteria are obtained in terms of linear matrix inequalities. Finally, two numerical examples are given to show the effectiveness of the proposed results.

Stochastic finite-time boundedness of Markovian jumping neural network with uncertain transition probabilities

The stochastic finite-time boundedness problem is considered for a class of uncertain Markovian jumping neural networks (MJNNs) that possess partially known transition jumping parameters. The transition of the jumping parameters is governed by a finite-state Markov process. By selecting the appropriate stochastic Lyapunov–Krasovskii functional, sufficient conditions of stochastic finite time boundedness of MJNNs are presented and proved. The boundedness criteria are formulated in the form of linear matrix inequalities and the designed algorithms are described as optimization ones. Simulation results illustrate the effectiveness of the developed approaches.

Delay-dependent state estimation for discrete Markovian jump neural networks with time-varying delay

The state estimation problem is discussed for discrete Markovian jump neural networks with time-varying delays in terms of linear matrix inequality (LMI) approach. The considered transition probabilities are assumed to be time-variant and partially unknown. The aim of the state estimation problem is to design a state estimator to estimate the neuron states and ensure the stochastic stability of the error-state system. A delay-dependent sufficient condition for the existence of the desired state estimator is proposed. An explicit expression of the desired estimator is also given. A numerical example is introduced to show the effectiveness of the given result.

State estimation for discrete Markovian jumping neural networks with time delay

The state estimation problem for discrete neural networks with Markovian jumping parameters and time-varying delays is investigated. The considered transition probabilities of the mode jumps are assumed to be partially unknown. The purpose of the state estimation problem is to design a state estimator to estimate the neuron states ensuring the dynamics of the estimation error stochastically stable. In terms of a novel Lyapunov functional, the delay-dependent sufficient conditions for the existence of desired state estimator are derived. A numerical example is given to show the validness of the established results.

Robust exponential stability of Markovian jumping neural networks with mode-dependent delay

This paper deals with the robust exponential stability problem for a class of Markovian jumping neural networks with time delay. The delay considered varies randomly, depending on the mode of the networks. By using a new Lyapunov–Krasovskii functional, a delay-dependent stability criterion is presented, which can be expressed in terms of linear matrix inequalities (LMIs). A numerical example is given to show the effectiveness of the results.

Stochastic asymptotic stability of Markovian jumping neural networks with Markov mode estimation and mode-dependent delays

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.

An LMI approach to stability analysis of stochastic high-order Markovian jumping neural networks with mixed time delays

This paper deals with the problem of global exponential stability for a general class of stochastic high-order neural networks with mixed time delays and Markovian jumping parameters. The mixed time delays under consideration comprise both discrete time-varying delays and distributed time-delays. The main purpose of this paper is to establish easily verifiable conditions under which the delayed high-order stochastic jumping neural network is exponentially stable in the mean square in the presence of both mixed time delays and Markovian switching. By employing a new Lyapunov–Krasovskii functional and conducting stochastic analysis, a linear matrix inequality (LMI) approach is developed to derive the criteria ensuring exponential stability. Furthermore, the criteria are dependent on both the discrete time-delay and distributed time-delay, and hence less conservative. The proposed criteria can be readily checked by using some standard numerical packages such as the Matlab LMI Toolbox. A simple example is provided to demonstrate the effectiveness and applicability of the proposed testing criteria.