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In this work, the recursive least squares (RLS) algorithm, which traditionally was used in the generalized predictive controller (GPC) framework solely for model adaptation purposes, was extended to cater for auto-tuning of the controller.
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The variable forgetting factor recursive least squares (VFF-RLS) algorithm was selected to capture the dynamics of the process online for the purpose of model adaptation in the controller.
Model-transferring domain adaptation methods use a previously trained source classifier for adaptation purposes on a target domain.
Acoustic model adaptation.
The single experiment included a preexposure stage for adaptation purposes.
Nevertheless, the feedback channel is not used for rate adaptation purposes.
Analyzing it becomes an important task particularly when impact analysis results are used for adaptation purposes.
Half of these data were used for supervised model adaptation.
Some of them are based on a model adaptation technique.
Model adaptation is demonstrated and shown to provide process insight.
This method derives a domain-invariant common subspace and learns representative cross-domain landmarks for adaptation purposes.
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