Approximate inference of nonparametric Hammerstein models

R.S. Risuleo, G. Bottegal, H. Hjalmarsson

Onderzoeksoutput: Bijdrage aan tijdschriftCongresartikelAcademicpeer review

2 Citaten (Scopus)
2 Downloads (Pure)


We propose a method for nonparametric identification of Hammerstein models with Gaussian-process models for the impulse response of the linear block and for the input nonlinearity. Interpreting the Gaussian-processes as prior distributions, we can estimate the unknowns using the posterior means given the data. To estimate the hyperparameters we set up an iterative scheme, reminiscent of the expectation-maximization method, where the posterior expectation of the complete likelihood is iteratively maximized. In the Hammerstein case, the posterior density is intractable because, in general, it does not admit a closed form expression. In this work, we propose two approximation approaches to estimate the posterior mean. In the first, we make a particle approximation of the posterior using Markov Chain Monte Carlo. In the second, we use a variational Bayes approach with a mean-field hypothesis. We validate the proposed methods on synthetic datasets of Hammerstein systems.

Originele taal-2Engels
Pagina's (van-tot)8333-8338
Aantal pagina's6
Nummer van het tijdschrift1
StatusGepubliceerd - 1 jul 2017


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