Abstract
The problem of identification of MISO Hammerstein model in case of correlated measurement noise is addressed. Because of the special structure of this kind of model, global convergence of the proposed estimation algorithm is proved while the model is nonlinear in the parameters. The analysis is in fact a generalisation of the work by M. Boutayeb et al. (1996) and consists first in transforming the nonlinear model into an input-output one linear in parameters. Afterwards, four successive stages based on the pseudo-inverse technique, are derived and lead us to a consistent estimator of the initial realisation as well as the model of the noise. Accuracy and performances of the proposed technique are shown through numerical examples with different signal to noise ratio values.
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