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These include robust estimation of mean and covariance in high dimensions, robust learning of various latent variable models, and robust supervised learning.
The use of a more fundamental methodology is also consistent with the reliable high fidelity physics models and robust, efficient, and accurate codes available today.
Our new approach is based on use of time varying triangular plant models and robust nonlinear and adaptive controllers that have been recently developed for such models.
reveals boundaries of stability, valid for a class of models and robust against uncertainties in specific models.
reveals boundaries of stability, valid for a class of models and robust against uncertainties in specific models avoids expensive numerical approximation of steady states and can be scaled to high-dimensional models.
Because measurement and simulation errors are unavoidable, we usually prefer robust models and robust parameter settings.
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Mixed-signal, RF and mm-wave circuit modeling and robust optimization.
An integrated modeling and robust model predictive control (MPC) approach is proposed for a class of nonlinear systems.
Reliable modeling and robust control of heat transfer processes are tasks with many applications in engineering.
This paper presents an empirical modeling and robust control of a novel meso-scale vapor compression refrigeration system (mVCRS).
Moreover, an application example is included to demonstrate uncertainty modeling and robust stability analysis used in control design.
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CEO of Professional Science Editing for Scientists @ prosciediting.com