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Search results for: MODEL ORDER SELECTION
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Automated Reduced Model Order Selection
PublicationThis letter proposes to automate generation of reduced-order models used for accelerated -parameter computation by applying a posteriori model error estimators. So far,a posteriori error estimators were used in Reduced Basis Method (RBM) and Proper Orthogonal Decomposition (POD) to select frequency points at which basis vectors are generated. This letter shows how a posteriori error estimators can be applied to automatically select...
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Automatic Reduction-Order Selection for Finite-Element Macromodels
PublicationAn automatic reduction-order selection algorithm for macromodels in finite-element analysis is presented. The algorithm is based on a goal-oriented a posteriori error estimator that operates on low-order reduced blocks of matrices, and hence, it can be evaluated extremely quickly.
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Identification of nonstationary multivariate autoregressive processes– Comparison of competitive and collaborative strategies for joint selection of estimation bandwidth and model order
PublicationThe problem of identification of multivariate autoregressive processes (systems or signals) with unknown and possibly time-varying model order and time-varying rate of parameter variation is considered and solved using parallel estimation approach. Under this approach, several local estimation algorithms, with different order and bandwidth settings, are run simultaneously and compared based on their predictive performance. First,...
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On joint order and bandwidth selection for identification of nonstationary autoregressive processes
PublicationWhen identifying a nonstationary autoregressive process, e.g. for the purpose of signal prediction or parametric spectrum estimation, two important decisions must be taken. First, one should choose the appropriate order of the autoregressive model, i.e., the number of autoregressive coefficients that will be estimated. Second, if identification is carried out using the local estimation technique, such as the localized version of...
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Multilevel model order reduction.
PublicationPrezentujemy wielopoziomowy algorytm redukcji rzędu modelu wykorzystany do zwiększenia efektywnosci analizy struktur mikrofalowych metodami siatkowymi.Schemat pozwala tworzyć makromodele zagniezdzone i laczyc te technike z szybkim przemiataniem częstotliwości (FFS). Implementacja metody pokazana jest na przukladzie różnic skończonych w dziedzinie częstotliwości i metody redukcji ENOR, lecz koncept moze być łatwo użyty w innych...
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Reduced order model of 2d system
PublicationA new method of modelling is developed for static and dynamic analysis of two-dimensional elastic bodies. In the analysis, an elastic body is divided into strips. For each one-dimensional strip the reduced modal model is build up. The modal model contains appropriate number of inputs and outputs to connect lumped interaction that occur between strips. Proposed method of modelling enables to obtain more accurate and more simple...
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Model order reduction for subgridding in fdtd scheme
PublicationW artykule zaprezentowana została technika pozwalająca na uzyskanie wysokiej rozdzielczości w metodzie FDTD. Prezentowany algorytm jest połączeniem metod redukcji rzędu modelu i lokalnych zagęszczeń zaimplementowanych do FDTD. Pozwala to zmniejszyć liczbę użytych zmiennych stanu, a także skrócić krok czasowy, co skutkuje znacznie krótszym czasem symulacji, niż w przypadku klasycznej metody FDTD.
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Grouping macromodels by using multilevel model order reduction
Publicationartykuł pprezentuje nowatorską technikę grupowania makromodeli dla metody fdtd. nowa technika bazuje na schemacie wielopoziomowej redukcji rzędu modeli. grupowanie makromodeli pozwala na zwiększenie szybkości symulacji w porównaniu do niezgrupowanych makromodeli, zapewniając przy tym porównywalną dokładność.
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Time domain validation of ultracapacitor fractional order model
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Auditory-model based robust feature selection for speech recognition
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