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This study investigates the problem of mode-independent H∞ filtering for a class of nonlinear systems with piecewise homogeneous Markovian jump process. Different from the existing studies in the literature, the existence of variations in transition rates for Markovian jump nonlinear systems is considered. The purpose of this paper is to design mode-independent filter, such that the dynamics of the...
In the paper, a novel method for identification of distributed parameter system (DPS) with recurrent trajectory via deterministic learning is proposed. The system dynamics rather than the system parameters or system structure is identified, which is completely different from the existing literature. The DPS, which is often described by partial differential equation (PDE), is first transformed into...
This paper is concerned with the H∞ filtering problem for a class of singular stochastic time-delay systems. The purpose is the design of a delay-independent filter such that the resulting error system is regular, impulse-free and mean-square stable. Sufficient conditions for solvability of this problem are formulated in terms of a set of linear matrix inequalities (LMIs), and stability theory of...
This paper studies the state estimation problem for a class of discrete-time nonlinear complex networks. A recursive state estimator is developed by employing the structure of the extended Kalman filter (EKF) with coupling terms. By using the stochastic analysis technique, an upper bound is derived for the coupling strength to guarantee the boundedness of the estimation errors in the mean square sense...
The issues of mean-square finite-time stability analysis and state estimator design for stochastic switched delayed neural networks are investigated in this paper. A stability criterion with average dwell time constraint is proposed, such that the mean-square value of state is not larger than a prescribed threshold during a given time interval. Then, a state estimator, which ensures mean-square finite-time...
In this paper, we investigate the state-based decentralized safe diagnosis issue of discrete-event systems. We first present a state-based decentralized safe diagnosis framework. Moreover, we introduce the notion of the state-based safe-codiagnosability, which satisfies both the diagnosability condition and the safety condition. In addition, a polynomial verification algorithm of the state-based safe-codiagnosability...
This paper is to investigate the linear minimum mean square error estimation for Markovian jump linear systems subject to unknown Markov chain modes, multi-channel observation delays, and data losses. Firstly, the original system is transformed into an extended system by defining a new state variable. The new state variable is concerned with the indicator function of the Markov chain and the original...
This paper presents an improved result on H∞ state estimation for static neural networks with a time-varying delay. First, a novel Lyapunov-Krasovskii functional (LKF) with several augmented terms is constructed. Then, a relaxed integral inequality is employed to make a tight estimation for single integral terms with time-varying delay in the derivative of the LKF. As a result, a delay-dependent criterion...
In this paper, the non-fragile state estimation problem is investigated for a class of discrete time-delay nonlinear complex networks with randomly occurring gain variations. Two sequences of random variables obeying the Bernoulli distribution are employed to describe the phenomena of randomly occurring time-varying delays and randomly occurring gain variations. Through stochastic analysis and Lyapunov...
This paper pay attention to the state estimation problem for a class of delayed neural networks with reaction-diffusion terms. By constructing a Lyapunov functional, together with the Hardy-Poincare inequality, a less conservative sufficient condition for the existence of state estimator is formulated in terms of linear matrix inequality (LMI). Finally, a numerical example is given to demonstrate...
This paper is concerned with the sampled-data state estimation problem for a class of delayed neural networks with randomly occurring sensor faults. Instead of the continuous measurements, the system measurements are sampled and then transmitted to the estimator to estimate the state of the neural network with simultaneous consideration of stochastic sensor faults. By employing the input delay approach,...
With the improvement of computer performance, particle filter as a method of state estimation, is widely used for nonlinear non-Gaussian dynamic systems. In order to reduce the partical degeneracy in the state estimation process of high-dimensional stochastic dynamic systems which commonly occurs in the conventional particle filter, an improved particle filter method based on adding more accurate...
A novel set-membership-based smoothing method for state estimation using the optimal bounding ellipsoid (OBE) algorithms is presented. OBE filters have been proven to be effective in state estimation problems with unknown but bounded errors. Compared with filtering methods, smoothing methods provide a much more accurate and reliable state estimate because observations beyond the current estimation...
This paper concentrates on the nonlinear filtering for systems with event-triggered data transmission and random time delays. A stochastic event-triggered sensor schedule is introduced to reduce the excessive measurements, which achieves a lower communication rate with a good estimation performance. The time delay is modeled by a Poisson distribution, and a D-length buffer is used to retrieve partly...
This paper is concerned with the multi-sensor optimal information fusion issues for discrete-time systems with random delayed which is time-stamped. The random delayed system is reconstructed as an equivalent delay-free one by means of the measurement reorganization technique when the random delay is time-stamped. Based on the altered system model, firstly an optimal linear filter is presented for...
State estimation and fault diagnosis are essential topics for dynamic systems. Unscented Kalman filter(UKF) has been widely applied in nonlinear systems. The classical UKF algorithm is built on the premise that process noise and measurement noise is independent. In practical problems, this assumption is not always satisfied. In addition, due to the limitation of communication and sensor fault, etc...
In this paper, a distributed Unscented Kalman Filter method (UKF) based on improved joint probabilistic data association (IJPDA) is proposed to solve the problem of nonlinear state estimation on multi-sensor multi-target tracking. Firstly, IJPDA method is introduced which determines the source of the multi-sensor data to simplify calculation complexity. Then the distributed UKF algorithm is presented...
In this paper, the event-triggered filtering problem is investigated for a class of nonlinear multi-rate systems. The nonlinearity is represented by a linear form with a norm-bounded uncertainty. In order to deal with the uncertainty resulting from the nonlinearities, a new augmentation approach is proposed to transform the multi-rate nonlinear system into a single-rate system. Based on the measurement...
Correct knowledge of noise statistics is essential for an effective estimator in maneuvering target tracking. In practice, however, the noise statistics are usually unknown or not perfectly known. To deal with the estimation problem in linear discrete-time systems with Markov jump parameters, where the measurement noise covariance is unknown, a novel approach is presented in this paper. This approach...
The extended target tracking (ETT) is widely used in various surveillance applications. In order to improve tracking accuracy and filtering efficiency of the existing Bernoulli filters, we present an improved Bernoulli filter in this paper. Firstly, the filtering equations of the proposed filter are analytically derived. After employing the weight optimization scheme, we discuss the particle implementation...
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