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The probabilistic model methods are Bayesian reasoning, evidence theory, robust statistics, recursive operators.
Based on high gain feedback control theory, robust adaptive synchronization of dynamical network is investigated in this paper.
Many problems occurring in engineering can be formulated as min max optimization problems, for instance, in game theory, robust optimal control and many others.
Based on signal compensation theory, robust controllers are designed to enhance both rotational and translational tracking performance, as well as robust performance over parameter uncertainty and external disturbance.
Convergence theory, robust numerical algorithms, and production quality mathematical software exist for general nonlinear systems of equations, and special cases such as Brouwer fixed point problems, polynomial systems, and nonlinear constrained optimization.
Many researchers try to apply probabilistic method, fuzzy method, info-gap decision theory, robust optimization and interval analysis for dealing with the DG uncertainty [54].
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Thus, it is natural to draw on the rich theory of robust linear regression [17 19, 64, 65] as a plausible approach to address the CS reconstruction problem when the measurements are contaminated with heavy-tailed noise.
Maximum likelihood (ML) type estimators (or more generally, -estimators) developed in the theory of robust statistics are of great importance in robust signal processing techniques [5].
Maximum likelihood (ML) type estimators, also known as M-estimators, developed in the theory of robust statistics are of great importance in robust signal processing techniques [14, 16].
Based on the Lyapunov stability theory, a robust controller is designed and its robustness and stability are analytically proved.
A μ theory of robust control is employed to design a stable controller for enhancing robust stability of the EHCLS in the presence of uncertainties of the inner loop, the control mechanism and the high frequency disturbance force.
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