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The analysis and modeling of networked datasets are probably the hottest research topics within the modern science of complex systems [1] [7].
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Under the control mode, the mathematical model of networked control systems is obtained.
This general powerful result ensures further implementation of model-based predictive control strategies based on state observers methods for the considered model of networked control systems.
The objective of this paper is to propose a systematic approach to decentralized stabilization with sampled-data delayed feedback for basic models of networked continuous-time complex systems.
Cloud storage, as a subservice of infrastructure as a service (IaaS) in cloud computing, is a model of networked online storage where data is stored in virtualized pools of storage.
On the basis of a case study, it is shown how formal models of networked automation architectures which are simple enough to be checked by existing timed model-checkers while yielding meaningful results can be developed.
First, a unified model of networked control system is proposed, which has two significant features: (a) the fault statistics of each sensor is individually quantified and (b) stochastic sensor faults and nonideal network Quality of Services (QoS), such as data dropout and network-induced delay, are coupled in a unified framework.
In this paper, modeling and control of networked control system with different loop topology structures are considered.
For simplicity these models that consist of networked fuzzy biological processes will be called of Bionet models.
Model-driven engineering of networked ambient systems (NAS) calls for advanced features in order to cope with the particular demands of hardware platforms, communication, and applications in the NAS domain.
Vertical fractures improved the recovery performance of the model in the case of networked fractures as compared to the case of horizontal fractures model.
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