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To facilitate practical implementation, model order of the controller is reduced by Henkel-norm method.
Since it is a first-order map, it preserves the model order of rational systems, making it a valuable frequency warping technique for use in audio filter design.
A design trade-off involves the approximation accuracy versus the complexity (model order) of the implemented filter.
In this article, a method is introduced to estimate the global autoregressive model order of a data set, based on the residuals after regression.
The computational complexity and identification time mainly depend on the number of speakers, the number of frame vectors, their dimensionality and the model order of the classifier.
Moreover, the model order of the underlying system is generally unknown and due to noise and model errors, usual statistical criteria cannot be used.
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But it also makes the Shell model orders of magnitude more computationally intensive, and therefore impractical for long-term or large-scale modeling (a similar level of computational intensity is required by the methods outlined in [21] and [25]).
The only difference is the penalty term (AIC uses k, the model order instead of Eq. 3).
So far the model order P of the multichannel AR process has been assumed to be known.
Model order reduction of parametric problems is a very active research field in systems theory, engineering and applied mathematics.
In ray-tracing based radio propagation prediction models, orders of diffractions are usually processed in a stage-by-stage fashion.
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