A comparison of two Hammerstein model identification algorithms
Two algorithms for least-squares estimation of parameters of a Hammerstein model are compared. Numerical examples demonstrate that the iterative method of Narendra and Gallman produces significantly smaller parameter covariance and slightly smaller rms error than the noniterative method of Chang and...
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| Published in | IEEE transactions on automatic control Vol. 21; no. 1; pp. 124 - 126 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.02.1976
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0018-9286 |
| DOI | 10.1109/TAC.1976.1101123 |
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| Abstract | Two algorithms for least-squares estimation of parameters of a Hammerstein model are compared. Numerical examples demonstrate that the iterative method of Narendra and Gallman produces significantly smaller parameter covariance and slightly smaller rms error than the noniterative method of Chang and Luus, as expected from an analysis of the parameter estimators. In addition, the iterative algorithm is faster for high-order systems. |
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| AbstractList | Two algorithms for least-squares estimation of parameters of a Hammerstein model are compared. Numerical examples demonstrate that the iterative method of Narendra and Gallman produces significantly smaller parameter covariance and slightly smaller rms error than the noniterative method of Chang and Luus, as expected from an analysis of the parameter estimators. In addition, the iterative algorithm is faster for high-order systems. |
| Author | Gallman, P. |
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| Cites_doi | 10.1109/TAC.1971.1099787 10.1109/TAC.1965.1098181 10.1109/TAC.1966.1098387 |
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| References | hsia (ref5) 1968 kalman (ref2) 1958 ref1 ref4 deutsch (ref6) 1965 ref3 |
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| Snippet | Two algorithms for least-squares estimation of parameters of a Hammerstein model are compared. Numerical examples demonstrate that the iterative method of... |
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| SubjectTerms | Asymptotic stability Control systems Controllability Differential equations Eigenvalues and eigenfunctions Iterative algorithms Matrices Numerical models Parameter estimation State feedback |
| Title | A comparison of two Hammerstein model identification algorithms |
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