In this paper, an extension of sensitivity based pruning (SBP) method for Nonlinear AutoRegressive models with eXogenous inputs (NARX) model is presented. Besides the in- puts, input and output delays are simultaneously pruned in terms of the backward elimination. The concept is based on replacement of some regressors by their mean value, which corresponds to the removal of inuence of the particular regressors from the network. The method is demonstrated on two datasets. Firstly, one artificial generator is used to test if the method is able to find an optimal set of inputs and delays. Further, the method is used for prediction of gas consumption of a simulated heating for an office building. It is shown that the SBP significantly reduces the complexity of the NARX network without any significant performance degradation. Moreover, it is hypothesized than SBP can be more important for NARX than for simple feedforward neural network, because NARX is more prone to overfitting and has problems with stability. © 2016 ICST.

Sensitivity based selection of inputs and delays for nonlinear ARX models

Moretti, F.
2016

Abstract

In this paper, an extension of sensitivity based pruning (SBP) method for Nonlinear AutoRegressive models with eXogenous inputs (NARX) model is presented. Besides the in- puts, input and output delays are simultaneously pruned in terms of the backward elimination. The concept is based on replacement of some regressors by their mean value, which corresponds to the removal of inuence of the particular regressors from the network. The method is demonstrated on two datasets. Firstly, one artificial generator is used to test if the method is able to find an optimal set of inputs and delays. Further, the method is used for prediction of gas consumption of a simulated heating for an office building. It is shown that the SBP significantly reduces the complexity of the NARX network without any significant performance degradation. Moreover, it is hypothesized than SBP can be more important for NARX than for simple feedforward neural network, because NARX is more prone to overfitting and has problems with stability. © 2016 ICST.
9781631901003
Feature selection;Time series prediction;Pruning;NARX;Neural networks
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12079/4194
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