Message Passing-based System Identification for NARMAX Models
| Autor: | Albert Podusenko, Semih Akbayrak, Ismail Senoz, Maarten Schoukens, Wouter M. Kouw |
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| Přispěvatelé: | Signal Processing Systems, Bayesian Intelligent Autonomous Systems, EAISI High Tech Systems, Autonomous Motion Control Lab, Control Systems, Cyber-Physical Systems Center Eindhoven, Machine Learning for Modelling and Control, Dynamic Networks: Data-Driven Modeling and Control |
| Rok vydání: | 2022 |
| Zdroj: | 2022 IEEE 61st Conference on Decision and Control (CDC), 7309-7314 STARTPAGE=7309;ENDPAGE=7314;TITLE=2022 IEEE 61st Conference on Decision and Control (CDC) |
| ISSN: | 7309-7314 |
| DOI: | 10.1109/cdc51059.2022.9992891 |
| Popis: | We present a variational Bayesian identification procedure for polynomial NARMAX models based on message passing on a factor graph. Message passing allows us to obtain full posterior distributions for regression coefficients, precision parameters and noise instances by means of local computations distributed according to the factorization of the dynamic model. The posterior distributions are important to shaping the predictive distribution for outputs, and ultimately lead to superior model performance during 1-step ahead prediction and simulation. |
| Databáze: | OpenAIRE |
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