6533b854fe1ef96bd12af5f5
RESEARCH PRODUCT
Input Selection Methods for Soft Sensor Design: A Survey
Giacomo FiumaraFrancesco CurreriMaria Gabriella Xibiliasubject
0209 industrial biotechnologylcsh:T58.5-58.64lcsh:Information technologyComputer Networks and CommunicationsComputer scienceFeature selectionprediction02 engineering and technologyFunction (mathematics)input selectionSoft sensorcomputer.software_genresoft sensor; inferential model; input selection; feature selection; regression; predictionfeature selection020901 industrial engineering & automationinferential model0202 electrical engineering electronic engineering information engineeringsoft sensorregression020201 artificial intelligence & image processingData miningInput selectioncomputerSelection (genetic algorithm)description
Soft Sensors (SSs) are inferential models used in many industrial fields. They allow for real-time estimation of hard-to-measure variables as a function of available data obtained from online sensors. SSs are generally built using industries historical databases through data-driven approaches. A critical issue in SS design concerns the selection of input variables, among those available in a candidate dataset. In the case of industrial processes, candidate inputs can reach great numbers, making the design computationally demanding and leading to poorly performing models. An input selection procedure is then necessary. Most used input selection approaches for SS design are addressed in this work and classified with their benefits and drawbacks to guide the designer through this step.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-06-04 | Future Internet |