Multiple adaptive mechanisms for data-driven soft sensors
| Autor: | Damien Fay, Bogdan Gabrys, Rashid Bakirov |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Engineering
business.industry General Chemical Engineering Real-time computing 02 engineering and technology Chemical Engineering Computer Science Applications Model correction Data-driven 0904 Chemical Engineering 0913 Mechanical Engineering 020401 chemical engineering Software deployment 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0204 chemical engineering Process industry Adaptation (computer science) business Real world data Selection (genetic algorithm) |
| ISSN: | 0098-1354 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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