Name
Abstract | ||
|---|---|---|
A general methodology for gray-box, or semi-physical, modeling is presented. This technique is intended to combine the best of two worlds: knowledge-based modeling, whereby mathematical equations are derived in order to describe a process, based on a physical (or chemical, biological, etc.) analysis, and black-box modeling, whereby a parameterized model is designed, whose parameters are estimated solely from measurements made on the process. The gray-box modeling technique is very valuable whenever a knowledge-based model exists, but is not fully satisfactory and cannot be improved by further analysis (or can only be improved at a very large computational cost). We describe the design methodology of a gray-box model, and illustrate it on a didactic example. We emphasize the importance of the choice of the discretization scheme used for transforming the differential equations of the knowledge-based model into a set of discrete-time recurrent equations. Finally, an application to a real, complex industrial process is presented. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S0893-6080(01)00096-X | Neural Networks |
Keywords | Field | DocType |
dynamic semi-physical modeling,gray box,knowledge base,physical model,differential equation,design methodology | Discretization,Chemical process modeling,Differential equation,Mathematical optimization,Parameterized complexity,Computer science,Algorithm,Design methods,Gray box testing,Knowledge base,Artificial neural network | Journal |
Volume | Issue | ISSN |
14 | 9 | 0893-6080 |
Citations | PageRank | References |
14 | 1.36 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Oussar | 1 | 294 | 26.32 |
| Gérard Dreyfus | 2 | 475 | 58.97 |