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An extended orthogonal forward regression algorithm for system identification using entropy.
- Source :
- International Journal of Control; Apr2008, Vol. 81 Issue 4, p690-699, 10p, 7 Charts, 8 Graphs
- Publication Year :
- 2008
-
Abstract
- In this paper, a fast identification algorithm for non-linear dynamic stochastic system identification is presented. The algorithm extends the classical orthogonal forward regression (OFR) algorithm so that instead of using the error reduction ratio (ERR) for term selection, a new optimality criterion, Shannon's entropy power reduction ratio (EPRR), is introduced to deal with both Gaussian and non-Gaussian signals. It is shown that the new algorithm is both fast and reliable and examples are provided to illustrate the effectiveness of the new approach. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00207179
- Volume :
- 81
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- International Journal of Control
- Publication Type :
- Academic Journal
- Accession number :
- 31499214
- Full Text :
- https://doi.org/10.1080/00207170701701031