Nan Hou

Non-fragile state estimation for discrete Markovian jumping neural networks

In this paper, the non-fragile state estimation problem is investigated for a class of discrete-time neural networks subject to Markovian jumping parameters and time delays. In terms of a Markov chain, the mode switching phenomenon at different times is considered in both the parameters and the discrete delays of the neural networks. To account for the possible gain variations occurring in the implementation, the gain of the estimator is assumed to be perturbed by multiplicative norm-bounded uncertainties. We aim to design a non-fragile state estimator such that, in the presence of all admissible gain variations, the estimation error converges to zero exponentially. By adopting the Lyapunov-Krasovskii functional and the stochastic analysis theory, sufficient conditions are established to ensure the existence of the desired state estimator that guarantees the stability of the overall estimation error dynamics. The explicit expression of such estimators is parameterized by solving a convex optimization problem via the semi-definite programming method. A numerical simulation example is provided to verify the usefulness of the proposed methods.

Event-triggered distributed state estimation for a class of time-varying systems over sensor networks with redundant channels

Abstract This paper is concerned with the distributed state estimation problem for a class of time-varying systems over sensor networks. An event-triggered communication scheme is utilized to save the constrained computation resource and network bandwidth while preserving the desired performance. The measurements on each node are transmitted to the estimators only when a certain triggering condition is satisfied. Moreover, in order to improve the reliability of data transmission services, we exploit redundant communication channels during the transmission process. The purpose of this paper is to design a set of time-varying state estimators such that the dynamics of the state estimation error satisfies the average H ∞ performance constraints. The specific gains of the estimator can be obtained by calculating a series of recursive linear matrix inequalities (RLMIs). Finally, a simulation example is presented to show the effectiveness of the state estimation method proposed in this paper.

Non-fragile H ∞ filtering for nonlinear systems with randomly occurring gain variations and channel fadings

In this paper, the H ∞ filtering problem is addressed for a class of discrete-time stochastic systems subject to randomly occurring gain variations (ROGVs), channel fadings, as well as randomly occurring nonlinearities (RONs). Due to the random nature of the occurrence of the gain variations in actual implementation, ROGVs, to be regulated by a group of random variables with Gaussian distribution, are utilized to better reflect this phenomenon. Then a L-th Rice fadings model is employed to represent channel fadings and time-delays simultaneously, in which the channel coefficients are mutually independent random variables conforming to any probability density function on 0, 1]. Furthermore, a kind of stochastic nonlinear disturbance is also considered in the H ∞ filtering research by means of a Bernoulli distributed white sequence. The purpose of this paper is to devise a non-fragile H ∞ filter such that the overall error dynamics is stochastically stable and satisfies the prescribed H ∞ performance. Through the Lyapunov stability theory, intensive stochastic analysis along with LMI methods, sufficient conditions are set up for the desired stochastic stability and H ∞ disturbance attenuation, and the proposed non-fragile filtering problem is converted into solving a convex optimization problem by the semidefinite programming technique. Finally, a numerical simulation example is provided to illustrate the effectiveness of the proposed design approach.

Variance-Constrained State Estimation for Complex Networks With Randomly Varying Topologies

This paper investigates the variance-constrained

H∞ state estimation for discrete-time neural networks with distributed delays and randomly occurring uncertainties through Fading channels

H_{\infty}

On passivity and robust passivity for discrete-time stochastic neural networks with randomly occurring mixed time delays

state estimation problem for a class of nonlinear time-varying complex networks with randomly varying topologies, stochastic inner coupling, and measurement quantization. A Kronecker delta function and Markovian jumping parameters are utilized to describe the random changes of network topologies. A Gaussian random variable is introduced to model the stochastic disturbances in the inner coupling of complex networks. As a kind of incomplete measurements, measurement quantization is taken into consideration so as to account for the signal distortion phenomenon in the transmission process. Stochastic nonlinearities with known statistical characteristics are utilized to describe the stochastic evolution of the complex networks. We aim to design a finite-horizon estimator, such that in the simultaneous presence of quantized measurements and stochastic inner coupling, the prescribed variance constraints on the estimation error and the desired

State estimation for discrete neural networks with randomly occurring uncertainties and missing measurements

H_{\infty}

Event-triggered state estimation for time-delayed complex networks with gain variations based on partial nodes

performance requirements are guaranteed over a finite horizon. Sufficient conditions are established by means of a series of recursive linear matrix inequalities, and subsequently, the estimator gain parameters are derived. A simulation example is presented to illustrate the effectiveness and applicability of the proposed estimator design algorithm.

Finite-horizon fault estimation under imperfect measurements and stochastic communication protocol: Dealing with finite-time boundedness: Finite-horizon fault estimation under imperfect measurements and stochastic communication protocol: Dealing with finite-time boundedness

In this paper, the H∞ state estimation problem is investigated for a class of uncertain discrete-time neural networks subject to infinitely distributed delays and fading channels. Randomly occurring uncertainties (ROUs) are introduced to reflect the random nature of the network condition fluctuations, and the channel fading phenomenon is considered to account for the possibly unreliable network medium on which the measurement signal is transmitted. A set of Bernoulli-distributed white sequences are employed to govern the ROUs and the L-th Rice fading model is utilized where channel coefficients are mutually independent random variables with certain probability density function on [0,1]. We aim to design a state estimator such that the dynamics of the estimation error is asymptotically stable while satisfying the prescribed H∞ performance constraint. By adopting the Lyapunov-Krasovskii functional and the stochastic analysis theory, sufficient conditions are established to ensure the existence of the desired state estimators and the explicit expression of such estimators is acquired. A simulation example is provided to verify the usefulness of the proposed approach.

Tobit Kalman filtering: Conditional expectation approach

In this paper, the passivity analysis problem is investigated for a class of discrete-time stochastic neural networks (DSNNs) with randomly occurring mixed time delays (ROMDs). The mixed delays comprise time-varying discrete delays, infinite-distributed delays as well as finite-distributed delays. A set of Bernoulli-distributed white sequences is used to account for the random nature of the occurrence of the mixed time delays. In addition, stochastic disturbances are taken into consideration to describe the state-dependent noises caused possibly by electronic devices and hardware implementation of neural networks. By using a combination of Lyapunov-Krasovskii functional, free-weighting matrix approach and stochastic analysis technique, we establish sufficient conditions guaranteeing the passivity performance of the underlying DSNNs. Furthermore, a delay-dependent robust passivity criterion is presented to deal with the parameter uncertainties in the DSNNs with ROMDs. A simulation example is provided to verify the effectiveness of the proposed approach.