Identification of Hammerstein-Wiener models
By Adrian G. Wills, Thomas Bo Schön, Lennart Ljung, Brett Ninness. at - Automatisierungstechnik, 2013
306 citations recorded by the source corpus.
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.
Subfields: Control and Systems Engineering, Signal Processing
View Paper: https://www.semanticscholar.org/paper/0640acb53f4d27aa1c45d67457377ab33e7adef6
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In Control and Systems Engineering
Citations in the corpus
- Nonlinear System Identification: A User-Oriented Road Map (2019, background): “Concrete system identification examples were there is significant process noise available are provided in for example [153], [159], [160].”