The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Identification of Hammerstein–Wiener models
Open accessAbstract
This paper develops and illustrates a new maximum-likelihood based method for the identification of Hammerstein-Wiener model structures. A central aspect is that a very general situation is considered wherein multivariable data, non-invertible Hammerstein and Wiener nonlinearities, and colored stochastic disturbances both before and after the Wiener nonlinearity are all catered for. The method developed here addresses the blind Wiener estimation problem as a special case.
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Citations
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Self-citation
Cited by
Allafi, W., Zajic, I., Uddin, K., & Burnham, K. J. (2017) · IET Control Theory & Applications · doi:10.1049/iet-cta.2017.0284 · Cited by 36
Paladino, O., Neviani, M., Ciancio, D., & De Francesco, M. (2024) · Biomass Conversion and Biorefinery · doi:10.1007/s13399-022-03634-3 · Cited by 7
Metadata
- Authors
- Wills, A., Schön, T. B., Ljung, L., & Ninness, B.
- Venue
- Automatica
- Publisher
- Elsevier BV
- Published
- 2013-01
- Volume
- 49
- Issue
- 1
- Pages
- 70-81
- ISSN
- 0005-1098 (ISSN-L)
- OpenAlex
- W2009061689
- Cited by
- 295
- References
- 31
- FWCI
- 19.94
- Type
- journal article
- Language
- en
- Open access
- green
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