<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si17.gif" display="inline" overflow="scroll"><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:math> filtering for uncertain stochastic time-delay systems with sector-bounded nonlinearities

Wang, Zidong; Liu, Yurong; Liu, Xiaohui

doi:10.1016/j.automatica.2007.09.016

Cited by 361 publications

(14 citation statements)

References 27 publications

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“…It should be pointed out that the main results presented in this paper can be extended to stability analysis, filter design and control applications for other discrete-time delayed systems (e.g. genetic regulatory networks), see [22][23][24][25][26][27] and the references therein.…”

Section: Discussionmentioning

confidence: 99%

State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays

2008

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In this paper, we investigate the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters as well as mode-dependent mixed time-delays. The parameters of the discrete-time neural networks are subject to the switching from one mode to another at different times according to a Markov chain, and the mixed time-delays consist of both discrete and distributed delays that are dependent on the Markovian jumping mode. New techniques are developed to deal with the mixed time-delays in the discrete-time setting, and a novel Lyapunov-Krasovskii functional is put forward to reflect the mode-dependent time-delays. Sufficient conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A numerical example is exploited to show the usefulness of the derived LMI-based conditions.

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Section: Discussionmentioning

confidence: 99%

State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays

2008

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“…In the preliminary research, a number of H 2 /H ∞ -based controllers or filters were proposed in different systems, such as the integral quadratic constraints system [17], stochastic uncertain systems [18], uncertain stochastic time-delay systems [19], aerospace system [20] and polytopic discrete-time systems [21]. Afterwards, kinds of the H 2 /H ∞ -based fusion filters were presented, which are not only used to estimate the collected data, but also combined the data fusion method [22,23], to increase the accuracy of the final estimate.…”

Section: Related Workmentioning

confidence: 99%

Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks

Zhang

Chao

2017

Sensors

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In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H2/H∞-based energy-efficient fusion estimation model (MHEEFE) for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance.

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“…Assumption 3 [35]. For ∀u, v ∈ R n , the nonlinear functions f (•), g(•) and h(•) satisfy the following sector-bounded condition:…”

Section: Complex Dynamical Network Model and Preliminariesmentioning

confidence: 99%

“…Thirdly, as is well known, a wide class of practical systems is influenced by additive nonlinear disturbances that are caused by environmental circumstances, the randomly occurring nonlinearities, which have recently received some interest in the literature. For example, in [35], the filtering and control problems for discrete-time systems with stochastic nonlinearities have been thoroughly investigated. Finally, in many practical systems, such as networked control systems, the probability distribution of time delay in the interval is an important characteristic for the network conditions [36]; the probability of the delay appearing in the lower interval is large and long delay happens with a low probability [37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Synchronization of Markovian jumping stochastic complex networks with distributed time delays and probabilistic interval discrete time-varying delays

Li¹,

Yue²

2010

J. Phys. A: Math. Theor.

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The paper investigates the synchronization stability problem for a class of complex dynamical networks with Markovian jumping parameters and mixed time delays. The complex networks consist of m modes and the networks switch from one mode to another according to a Markovian chain with known transition probability. The mixed time delays are composed of discrete and distributed delays, the discrete time delay is assumed to be random and its probability distribution is known a priori. In terms of the probability distribution of the delays, the new type of system model with probability-distribution-dependent parameter matrices is proposed. Based on the stochastic analysis techniques and the properties of the Kronecker product, delay-dependent synchronization stability criteria in the mean square are derived in the form of linear matrix inequalities which can be readily solved by using the LMI toolbox in MATLAB, the solvability of derived conditions depends on not only the size of the delay, but also the probability of the delay-taking values in some intervals. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method.

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H∞ filtering for uncertain stochastic time-delay systems with sector-bounded nonlinearities

Cited by 361 publications

References 27 publications

State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays

State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays

Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks

Synchronization of Markovian jumping stochastic complex networks with distributed time delays and probabilistic interval discrete time-varying delays

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