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Paper formulates the problem of multiobjective probabilistic mixture control design and proposes its general solution with both system model and target represented by finite probabilistic mixtures.
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In this paper, adaptive neural control is presented for a class of switched nonlinear systems with switching jumps and uncertainties in both system models and switching signals.
The ErbB network is one of the most extensively studied areas of signal transduction, serving as an archetype for both systems modeling and molecular oncology.
In this section, both the radar system model and the FH communication system model are introduced to formulate the problem.
Thus, the information of both the system model and the measurement data can be applied effectively.
Both the system model and problem formulation of heterogeneous cellular networks are proposed in Section 1.
A hybrid dynamic simulation method is proposed in [66, 67] to locate the oscillation source, which is based on both the system model and the measurement data.
Both system models with and without relaying are considered.
Section 2 introduces the system model and describes both classical ED algorithm and the blind SP-CAF sensing method, respectively.
Both the system models and the mathematical framework are implemented in MS Excel.
Through both dynamic system modeling and extensive simulation studies, we show that the FC-MAC algorithm is stable and achieves fairness while improving the aggregate throughput.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com