Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6469238 | Computers & Chemical Engineering | 2017 | 13 Pages |
â¢Data-driven soft sensors often include multiple mechanisms for their adaptation.â¢Deploying these adaptive mechanisms in prescribed order is often inferior.â¢Flexible order of adaptive mechanisms leads to a higher predictive accuracy.â¢Cross-validation is successfully used as a basis for the adaptation choice.â¢Retrospective adaptation for model optimization further boosts performance.
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.