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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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
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Lennart Ljung, A. (1987) · Cited by 17,690
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,502
System identification—Theory for the user
Sinha, N. K. (1989) · Automatica · doi:10.1016/0005-1098(89)90019-8 · Cited by 9,184
Numerical Methods for Unconstrained Optimization and Nonlinear Equations
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(2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9 · Cited by 7,366
The EM Algorithm and Extensions
Kushary, D. (1998) · Technometrics · doi:10.1080/00401706.1998.10485534 · Cited by 5,417
Beyond the Kalman filter particle filters for tracking applications
Ristić, B., Arulampalam, S., & Gordon, N. (2004) · Cited by 3,372
The Volterra and Wiener Theories of Nonlinear Systems
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Subspace identification for linear systems: theory, implementation, applications
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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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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
