Particle filters for recursive model selection in linear and nonlinear system identification

Recursive model selection can be addressed within the Bayesian framework, the multiple model algorithm being one such approach for linear Gaussian systems. The recent advances in nonlinear non-Gaussian estimation with the sequential Monte Carlo algorithms such as the particle filter allow the application of Bayesian inference to the development of recursive model selection algorithms for general nonlinear non-Gaussian systems. Such an algorithm is developed in this paper and applied to a linear auto-regressive (AR) and nonlinear auto-regressive (NAR) systems.