Structure identification of nonlinear dynamic systems—a survey on input/output approaches
Automatica (Journal of IFAC)
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Modelling and identification of non-linear deterministic systems in the delta-domain
Automatica (Journal of IFAC)
Bayesian system identification via Markov chain Monte Carlo techniques
Automatica (Journal of IFAC)
System identification of nonlinear state-space models
Automatica (Journal of IFAC)
Brief paper: Structure detection and parameter estimation for NARX models in a unified EM framework
Automatica (Journal of IFAC)
Joint Bayesian model selection and estimation of noisy sinusoidsvia reversible jump MCMC
IEEE Transactions on Signal Processing
Bayesian system identification
Automatica (Journal of IFAC)
A two-stage algorithm for identification of nonlinear dynamic systems
Automatica (Journal of IFAC)
On the efficiency of the orthogonal least squares training method for radial basis function networks
IEEE Transactions on Neural Networks
| Hi-index | 22.14 |
In this contribution we derive a computational Bayesian approach to NARMAX model identification. The identification algorithm exploits continuing advances in computational processing power to numerically obtain posterior distributions for both model structure and parameters via sampling methods. The main advantage of this approach over other NARMAX identification algorithms is that for the first time model uncertainty is characterised as a byproduct of the identification procedure. The algorithm is based on the reversible jump Markov chain Monte Carlo (RJMCMC) procedure. Key features of the approach are (i) sampling of unselected model terms for testing for inclusion in the model (the birth move), which encourages global searching of the model term space, (ii) sampling of previously selected model terms for testing for exclusion from the model-a naturally incorporated pruning step (the death move), which leads to model parsimony, and (iii) estimation of model and parameter distributions, which are naturally generated in the Bayesian framework. We present a numerical example to demonstrate the algorithm and a comparison with a forward regression method: the results show that the RJMCMC approach is competitive and gives useful additional information regarding uncertainty in both model parameters and structure.