tailieunhanh - Adaptive lọc và phát hiện thay đổi P6

Model validation is the problem of deciding whether observed data are consistent with a nominal model . Change detection based on model validation aims at applying a consistency test in one of the following ways: | Adaptive Filtering and Change Detection Fredrik Gustafsson Copyright 2000 John Wiley Sons Ltd ISBNs 0-471-49287-6 Hardback 0-470-84161-3 Electronic __6 Change detection based on sliding windows . . Distance . Prediction . Generalized likelihood . Information based norms .212 . The divergence test .212 . The asymptotic local . General parallel . Likelihood based detection and . . A general . Diagnosis of parameter and variance . Design . Applications .227 . Rat . Belching . Application to digital communication .229 . Basics Model validation is the problem of deciding whether observed data are consistent with a nominal model. Change detection based on model validation aims at applying a consistency test in one of the following ways The data are taken from a sliding window. This is the typical application of model validation. The data are taken from an increasing window. This is one way to motivate the local approach. The detector becomes more sensitive when the data size increases by looking for smaller and smaller changes. 206 Change detection based on sliding windows The nominal model will be represented by the parameter vector 0q. This may be obtained in one of the following ways 0q is recursively identified from past data except for the ones in the sliding window. This will be our typical case. 0q corresponds to a nominal model obtained from physical modeling or system identification. The standard setup is illustrated in Data i j 2 yt-L yt-L 1 Model 0o Model 0 A model 0 based on data from a sliding window of size L is compared to a model o based on all past data or a substantially larger sliding window. Let us denote the vector of L measurements in the sliding window by Y yJ_ yt T. Note the convention .

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