Bakirov, R., Gabrys, B. and Fay, D., 2017. Multiple Adaptive Mechanisms for Data-driven Soft Sensors. Computers and Chemical Engineering, 96 (January), pp. 42-54.
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Recent data-driven soft sensors often use multiple adaptive mechanisms to cope with non-stationary environments. These mechanisms are usually deployed in a prescribed order which does not change. In this work we use real world data from the process industry to compare deploying adaptive mechanisms in a fixed manner to deploying them in a flexible way, which results in varying adaptation sequences. We demonstrate that flexible deployment of available adaptive methods coupled with techniques such as cross-validatory selection and retrospective model correction, can benefit the predictive accuracy over time. As a vehicle for this study, we use a soft-sensor for batch processes based on an adaptive ensemble method which employs several adaptive mechanisms to react to the changes in data.
|Uncontrolled Keywords:||Soft sensors; Adaptive mechanisms; Streaming data; Ensemble method|
|Group:||Faculty of Science & Technology|
|Deposited By:||Unnamed user with email symplectic@symplectic|
|Deposited On:||01 Sep 2016 15:36|
|Last Modified:||05 Dec 2016 10:04|
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