Identification of Wiener-Hammerstein models based on variational Bayesian approach in the presence of process noise

DOI10.1016/J.JFRANKLIN.2021.05.003zbMath1467.93319OpenAlexW3163160816MaRDI QIDQ2041405

Yanyan Li

Publication date: 19 July 2021

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2021.05.003

Mathematics Subject Classification ID

Identification in stochastic control theory (93E12)

Uses Software

• System Identification Toolbox
• Wiener-Hammerstein Benchmark
• PRMLT

Cites Work

• Unnamed Item
• System identification of nonlinear state-space models
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• Identification of block-oriented nonlinear systems starting from linear approximations: a survey
• A two-experiment approach to Wiener system identification
• An improved method for Wiener-Hammerstein system identification based on the fractional approach
• Maximum likelihood identification of Wiener models
• Identification of Wiener-Hammerstein systems by a nonparametric separation of the best linear approximation
• Nonlinear System Identification

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