A bayesian framework for large-scale identification of nonlinear hybrid systems
In this paper, a two-level Bayesian framework is proposed for the identification of nonlinear
hybrid systems from large data sets by embedding it in a four-stage procedure. At the first
stage, feature vector selection techniques are used to generate a reduced-size set from the
given training data set. The resulting data set then is used to identify the hybrid system using
a Bayesian method, where the objective is to assign each data point to a corresponding sub-
mode of the hybrid model. At the third stage, this data assignment is used to train a Bayesian …
hybrid systems from large data sets by embedding it in a four-stage procedure. At the first
stage, feature vector selection techniques are used to generate a reduced-size set from the
given training data set. The resulting data set then is used to identify the hybrid system using
a Bayesian method, where the objective is to assign each data point to a corresponding sub-
mode of the hybrid model. At the third stage, this data assignment is used to train a Bayesian …
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