A randomised approach for NARX model identification based on a multivariate Bernoulli distribution

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...

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Bibliographic Details
Published in:International journal of systems science Vol. 48; no. 6; pp. 1203 - 1216
Main Authors: Bianchi, F., Falsone, A., Prandini, M., Piroddi, L.
Format: Journal Article
Language:English
Published: London Taylor & Francis 26.04.2017
Taylor & Francis Ltd
Subjects:
Algorithms
Assessments
Basic converters
Bernoulli Hypothesis
Complexity
Conditioning
Convergence
Correlation
model structure selection
multivariate Bernoulli distribution
Nonlinear model identification
polynomial NARX models
Polynomials
prediction error minimisation methods
Probabilistic methods
Probability theory
randomised algorithms
Robustness
Sampling
Tasks
ISSN:0020-7721, 1464-5319
Online Access:Get full text
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Summary: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.
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ISSN:0020-7721
1464-5319
DOI:10.1080/00207721.2016.1244309