Sentence examples for both stochastic and robust from inspiring English sources

Both stochastic and robust models result in cost saving since they decide to purchase in the best possible period in advance.

The two most common approaches to address these challenges — stochastic and robust optimization — have drawbacks that restrict their application to real-world systems.

Evolutionary algorithms (EAs) – paradigms of evolutionary computation field – are stochastic and robust meta-heuristics useful to solve reliability redundancy optimization problems.

Evolutionary Algorithms (EAs) are stochastic and robust meta-heuristics of evolutionary computation field useful to solve optimization problems in image processing applications.

The proposed approaches derive sufficient conditions such that the regularity and the absence of impulses are assured as well as the stochastic stability and robust stochastic stability.

This paper deals with the problems of non-fragile robust stochastic stabilization and robust H∞ control for uncertain stochastic nonlinear time-delay systems.

This paper investigates the problems of robust stochastic stabilization and robust H∞ control for uncertain neutral stochastic time-delay systems with time-varying norm-bounded parameter uncertainties appearing in both the state and input matrices.

This paper considers the problems of robust stochastic stabilization and robust H∞ controller design for a class of stochastic Markovain jumping systems with mixed time delays and polytopic parameter uncertainties.

In this study, a novel stochastic analysis and robust optimization approach is proposed to improve the stamping robustness, where the uncertainties are involved to reflect manufacturing reality.

Furthermore, in the case when time delays appear in both the state and control input, results on the robust stochastic stabilization and robust H∞ control are also presented.

We provide complete results for robust weak-dependent stochastic stability and robust linear filter design.