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Resilient H-infinity filtering for networked nonlinear Markovian jump systems with randomly occurring distributed delay and sensor saturation Nonlinear Anal. Model. Control (IF 2.78) Pub Date : 2021-03-01 Venkatesan Nithya; Rathinasamy Sakthivel; Yong Ren
The H∞ filtering problem for a class of networked nonlinear Markovian jump systems subject to randomly occurring distributed delays, nonlinearities, quantization effects, missing measurements and sensor saturation is investigated in this paper. The measurement missing phenomenon is characterized via a random variable obeying the Bernoulli stochastic distribution. Moreover, due to bandwidth limitations
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Eventually periodic solutions of single neuron model Nonlinear Anal. Model. Control (IF 2.78) Pub Date : 2020-11-01 Inese Bula; Michael A. Radin
In this paper, we consider a nonautonomous piecewise linear difference equation that describes a discrete version of a single neuron model with a periodic (period two and period three) internal decay rate. We investigated the periodic behavior of solutions relative to the periodic internal decay rate in our previous papers. Our goal is to prove that this model contains a large quantity of initial conditions
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Dynamics for a stochastic delayed SIRS epidemic model Nonlinear Anal. Model. Control (IF 2.78) Pub Date : 2020-09-01 Xiangyun Shi; Yimeng Cao; Xueyong Zhou
In this paper, we consider a stochastic delayed SIRS epidemic model with seasonal variation. Firstly, we prove that the system is mathematically and biologically well-posed by showing the global existence, positivity and stochastically ultimate boundneness of the solution. Secondly, some sufficient conditions on the permanence and extinction of the positive solutions with probability one are presented
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Exponential state estimation for competitive neural network via stochastic sampled-data control with packet losses Nonlinear Anal. Model. Control (IF 2.78) Pub Date : 2020-07-01 Xin Sui; Yongqing Yang; Fei Wang
This paper investigates the exponential state estimation problem for competitive neural networks via stochastic sampled-data control with packet losses. Based on this strategy, a switched system model is used to describe packet dropouts for the error system. In addition, transmittal delays between neurons are also considered. Instead of the continuous measurement, the sampled measurement is used to