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The theoretical results are applied to a controller design problem based on stochastic linear, longitudinal F16 model.
An effective distributed algorithm is proposed based on stochastic linear quadratic optimal control theory assuming SIR measurements contain white noise.
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The solution method is based on stochastic successive linear programming.
Based on stochastic analysis and linear matrix inequality techniques, sufficient conditions of stochastic finite-time boundedness and stochastic H∞ finite-time boundedness are firstly derived for the existence of the desired estimator.
The response surface method based on stochastic process is used, that is especially good at modeling the non-linear, multi-modal functions that often bring about in engineering.
Next, based on stochastic Lyapunov stabilization analysis, sufficient conditions for the existence of a PI tracking control are established for each linear model in terms of linear matrix inequalities.
NORMA is based on stochastic gradient descent.
Reliability analysis is based on stochastic discrete event models like stochastic Petri nets.
The algorithm is developed based on stochastic finite-element method.
Our mechanism is based on stochastic datagram relocation.
KF technique makes predictions based on stochastic and dynamic structures.
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