The problem of asynchronous and resilient filtering for discrete-time Markov jump neural networks subject to extended dissipativity is investigated in this paper. The modes of the designed resilient filter are assumed to run asynchronously with the modes of original Markov jump neural networks, which accord well with practical applications and are described through a hidden Markov model. Due to the fluctuation of the filter parameters, a resilient filter taking into account parameter uncertainty is adopted. Being different from the norm-bound type of uncertainty which has been studied in a considerable number of the existing literatures, the interval type of uncertainty is introduced so as to describe uncertain phenomenon more accurately. By means of convex optimal method, the gains of filter are derived to guarantee the stochastic stability and extended dissipativity of the filtering error system under the wave of the filter parameters. Considering the limited computing power of MATLAB solver, a relatively simple simulation is exploited to verify the effectiveness and merits of the theoretical findings where the relationships among optimal performance index, uncertain parameter σ, and asynchronous rate are revealed.

This paper studies the issue of robust state estimation for coupled neural networks with parameter uncertainty and randomly occurring distributed delays, where the polytopic model is employed to describe the parameter uncertainty. A set of Bernoulli processes with different stochastic properties are introduced to model the randomly occurrences of the distributed delays. Novel state estimators based on the local coupling structure are proposed to make full use of the coupling information. The augmented estimation error system is obtained based on the Kronecker product. A new Lyapunov function, which depends both on the polytopic uncertainty and the coupling information, is introduced to reduce the conservatism. Sufficient conditions, which guarantee the stochastic stability and the l ₂ -l _∞ performance of the augmented estimation error system, are established. Then, the estimator gains are further obtained on the basis of these conditions. Finally, a numerical example is used to prove the effectiveness of the results.

The problem of dissipativity-based resilient filtering for discrete-time periodic Markov jump neural networks in the presence of quantized measurements is investigated in this paper. Due to the limited capacities of network medium, a logarithmic quantizer is applied to the underlying systems. Considering the fact that the filter is realized through a network, randomly occurring parameter uncertainties of the filter are modeled by two mode-dependent Bernoulli processes. By establishing the mode-dependent periodic Lyapunov function, sufficient conditions are given to ensure the stability and dissipativity of the filtering error system. The filter parameters are derived via solving a set of linear matrix inequalities. The merits and validity of the proposed design techniques are verified by a simulation example.

This article focuses on the investigation of finite-time dissipative state estimation for Markov jump neural networks. First, in view of the subsistent phenomenon that the state estimator cannot capture the system modes synchronously, the hidden Markov model with partly unknown probabilities is introduced in this article to describe such asynchronization constraint. For the upper limit of network bandwidth and computing resources, a novel dynamic event-triggered transmission mechanism, whose threshold parameter is constructed as an adjustable diagonal matrix, is set between the estimator and the original system to avoid data collision and save energy. Then, with the assistance of Lyapunov techniques, an event-based asynchronous state estimator is designed to ensure that the resulting system is finite-time bounded with a prescribed dissipation performance index. Ultimately, the effectiveness of the proposed estimator design approach combining with a dynamic event-triggered transmission mechanism is demonstrated by a numerical example.

This paper focuses on the dissipativity-based asynchronous filtering problem for a class of discrete-time Takagi-Sugeno (T-S) fuzzy Markov jump systems (FMJSs) subject to randomly occurred quantization. Considering the random fluctuations of network conditions, the randomly occurred quantization is introduced to describe the quantisation phenomenon appearing in a probabilistic way. To take full advantage of partial information of system modes for the desired system performance, we adopt the asynchronous filter in which mode transition matrix is nonhomogeneous. The mode dependent time-varying delays are introduced which have different bounds for different system modes. Via fuzzy-mode-dependent Lyapunov functional approach which can reduce conservatism, a sufficient condition on the existence of the asynchronous filter is derived such that the filtering error system is stochastically stable and strictly (Q; S;R)-dissipative. Then, the gains of the filter are obtained by solving a set of linear matrix inequalities (LMIs). An example is utilized to illustrate the validity of the developed filter design technique where the relationships among optimal dissipative performance indices, delays, quantization parameter and the degree of asynchronous jumps are given.

In order to save network resources of discrete-time Markov jump systems, an event-triggered control framework is employed in this article. The threshold parameter in the event-triggered mechanism is designed as a diagonal matrix in which all elements can be adjusted according to system performance requirements. The hidden Markov model is introduced to characterize the asynchronization between the controller and controlled system. The effect of randomly occurring gain fluctuations is taken into account during the controller design. For the purpose of guaranteeing that the closed-loop system is stochastically stable and satisfies the strictly $(\mathcal {D}_{1},\mathcal {D}_{2},\mathcal {D}_{3})-\gamma -$ dissipative performance, sufficient conditions are constructed by employing the Lyapunov function and stochastic analysis. After linearization, the proposed controller gains are obtained by solving the linear matrix inequalities. Ultimately, a practical example of the dc motor device is used to illustrate the effectiveness of the proposed new design technique.