In this study, dedicated methods of parameter estimation were used to identify the coefficients of continuous models represented by linear and nonlinear differential equations. The necessary discrete-time approximation of the base model is achieved by appropriately tuned FIR linear integral filters. The resulting discrete descriptions, which retain the original continuous parameterization, can then be identified using the classical least squares procedure. Since, in the presence of correlated noise, the relevant parameter estimates suffer from an asymptotic systematic error, the instrumental variable method is used here to significantly improve the consistency of the estimates. The finally applied algorithm based on the criterion of the smallest sum of absolute values is used to identify linear and nonlinear models in the presence of sporadic measurement errors. In summary, the effectiveness of the proposed solutions is demonstrated by means of numerical tests.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1007/978-3-031-16159-9_26
- Category
- Publikacja monograficzna
- Type
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Language
- angielski
- Publication year
- 2022
