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Distributed randomized model structure selection for NARX models
Author(s) -
Avellina M.,
Brankovic A.,
Piroddi L.
Publication year - 2017
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2803
Subject(s) - nonlinear autoregressive exogenous model , computer science , autoregressive model , convergence (economics) , mathematical optimization , selection (genetic algorithm) , set (abstract data type) , nonlinear system , flexibility (engineering) , model selection , computational complexity theory , algorithm , mathematics , artificial intelligence , artificial neural network , statistics , physics , quantum mechanics , economics , econometrics , programming language , economic growth
Summary Model structure selection (MSS) is a critical problem in the nonlinear identification field. In the framework of polynomial nonlinear autoregressive [moving average] models with exogenous input variables, it is formulated as the combinatorial problem of finding the subset of regressors that yields optimal model accuracy. Increasing the set of potential model terms improves the flexibility of the model but results in a computational overload and may even jeopardize the ability of the MSS algorithm to find the optimal model. In this work, a distributed optimization scheme is developed to tackle the MSS task for large‐sized candidate regressor sets. The regressor set is split among a group of independent processors, and each of them executes an MSS routine on its local subset. Then, the processors exchange information regarding the selected models, and the corresponding regressors are distributed among all the units for a new MSS round. The procedure is repeated until convergence of all processors to the same solution. Besides a drastic reduction in the computational time, thanks to the inherent parallelizability of the algorithm execution, the proposed distributed optimization scheme can also be beneficial in terms of model accuracy, due to a more efficient exploration of the search space.