A new form of prediction error method (PEM) is developed. It is applicable to the case where the model structure of interest can be imbedded in a larger model structure whose estimation is relatively easy. An optimal way of reducing the larger model to the smaller model structure is presented and va
Model approximations via prediction error identification
β Scribed by B.D.O. Anderson; J.B. Moore; R.M. Hawkes
- Publisher
- Elsevier Science
- Year
- 1978
- Tongue
- English
- Weight
- 613 KB
- Volume
- 14
- Category
- Article
- ISSN
- 0005-1098
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β¦ Synopsis
Identification is considered of a dynamic system by a model in a model set of which the system is not a member. This is achieved by defining a performance index related to prediction error performance indices, and taking that model minimizing the performance index as that which is closest to the system. The index has an intuitively pleasing spectral interpretation in the stationary case for large measurement intervals. The length of measurement interval needed for identification is discussed by studying the limiting behaviour of the performance indices, as is also the relation of the index to the Kullback information measure. The communication theoretic issue of convergence of a posteriori densities when Bayesian estimation is being undertaken with a finite model set is examined.
π SIMILAR VOLUMES
## Abstract Classical prediction error minimization (PEM) methods are widely used for model identification, but they are also known to provide satisfactory results only in specific identification conditions, e.g. disturbance model matching. If these conditions are not met, the obtained model may ha
A general recursive identification algorithm is found to have the same convergence properties as a family of off-line prediction error methods.