Identification of combined physical and empirical models using nonlinear A priori knowledge
| Autor: | Duncan A. Mellichamp, Andreas Hans Kemna |
|---|---|
| Rok vydání: | 1995 |
| Předmět: |
business.industry
Computer science Applied Mathematics System identification Empirical modelling Process (computing) Work in process Machine learning computer.software_genre Computer Science Applications Nonlinear system Identification (information) Data extraction Control and Systems Engineering A priori and a posteriori Artificial intelligence Data mining Electrical and Electronic Engineering business computer |
| Zdroj: | Control Engineering Practice. 3:375-382 |
| ISSN: | 0967-0661 |
| Popis: | The incorporation of a priori knowledge in process identification is discussed. Using a form of prefiltering, referred to here as data extraction, the effects of dynamics known a priori are removed from available input/output process data. The resulting information is then used to identify the remaining unknown process dynamics. This approach to system identification has important advantages — particularly with regard to noise sensitivity, necessary amount of excitation, and the complexity of the model to be estimated. The data extraction method in its nonlinear version is applied in simulations and to actual data from a bench-scale and an industrial process. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect