Message Passing-based System Identification for NARMAX Models
- Resource Type
- Conference
- Authors
- Podusenko, Albert; Akbayrak, Semih; Senoz, Ismail; Schoukens, Maarten; Kouw, Wouter M.
- Source
- 2022 IEEE 61st Conference on Decision and Control (CDC) Decision and Control (CDC), 2022 IEEE 61st Conference on. :7309-7314 Dec, 2022
- Subject
- Robotics and Control Systems
Computational modeling
Message passing
Predictive models
Bayes methods
System identification
- Language
- ISSN
- 2576-2370
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.