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Nonparametric models for Hammerstein-Wiener and Wiener-Hammerstein system identification
- Publication Year :
- 2020
-
Abstract
- We propose a framework for modeling structured nonlinear systems using nonparametric Gaussian processes. In particular, we introduce a two-layer stochastic model of latent interconnected Gaussian processes suitable for modeling Hammerstein-Wiener and Wiener-Hammerstein cascades. The posterior distribution of the latent processes is intractable because of the nonlinear interactions in the model; hence, we propose a Markov Chain Monte Carlo method consisting of a Gibbs sampler where each step is implemented using elliptical-slice sampling. We present the results on two example nonlinear systems showing that they can effectively be modeled and identified using the proposed nonparametric modeling approach.
Details
- Database :
- OAIster
- Notes :
- Risuleo, Riccardo, Hjalmarsson, Hakan
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1293950684
- Document Type :
- Electronic Resource
- Full Text :
- https://doi.org/10.1016.j.ifacol.2020.12.198