A randomised approach for NARX model identification based on a multivariate Bernoulli distribution. Issue 6 (26th April 2017)

• Record Type: Journal Article
• Authors: Bianchi, F.; Falsone, A.; Prandini, M.; Piroddi, L.
• Is Part Of: International journal of systems science. Volume 48:Issue 6(2017)
• Page Start: 1203
• Page End: 1216
• Publication Date: 2017-04-26
• Subjects: Nonlinear model identification -- model structure selection -- polynomial NARX models -- randomised algorithms -- multivariate Bernoulli distribution -- prediction error minimisation methods; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2016.1244309
• Languages: English
• ISSNs: 0020-7721

Abstract

ABSTRACT: The identification of polynomial NARX models is typically performed by incremental model building techniques. These methods assess the importance of each regressor based on the evaluation of partial individual models, which may ultimately lead to erroneous model selections. A more robust assessment of the significance of a specific model term can be obtained by considering ensembles of models, as done by the RaMSS algorithm. In that context, the identification task is formulated in a probabilistic fashion and a Bernoulli distribution is employed to represent the probability that a regressor belongs to the target model. Then, samples of the model distribution are collected to gather reliable information to update it, until convergence to a specific model. The basic RaMSS algorithm employs multiple independent univariate Bernoulli distributions associated to the different candidate model terms, thus overlooking the correlations between different terms, which are typically important in the selection process. Here, a multivariate Bernoulli distribution is employed, in which the sampling of a given term is conditioned by the sampling of the others. The added complexity inherent in considering the regressor correlation properties is more than compensated by the achievable improvements in terms of accuracy of the model selection process.