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On the selection of ınterpolation points for rational Krylov Methods
(Springer-Verlag Berlin, 2012)
We suggest a simple and an efficient way of selecting a suitable set of interpolation points for the well-known rational Krylov based model order reduction techniques. To do this some sampling points from the frequency ...
Applications of Eigenvalue Counting and Inclusion Theorems in Model Order Reduction
(Springer-Verlag Berlin, 2010)
We suggest a simple and an efficient iterative method based on both the Gerschgorin eigenvalue inclusion theorem and the deflation methods to compute a Reduced Order Model (ROM) to lower greatly the order of a given state ...
Feature Extraction Based on Deep Learning for Some Traditional Machine Learning Methods
(Institute of Electrical and Electronics Engineers Inc., 2018)
Deep learning is a subfield of machine learning and deep neural architectures can extract high level features automatically without handcraft feature engineering unlike traditional machine learning algorithms. In this ...