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Model selection for RBF-ARX models
Chen, Qiong Ying1,2; Chen, Long3; Su, Jian Nan1; Fu, Ming Jian1; Chen, Guang Yong1
Source PublicationApplied Soft Computing

Radial basis function network-based autoregressive with exogenous input (RBF-ARX) models are useful in nonlinear system modelling and prediction. The identification of RBF-ARX models includes optimization of the (model lags, number of hidden nodes and state vector) and the parameters of the model. Previous works have usually ignored optimizations of the model's architecture. In this paper, the RBF-ARX architecture, which includes the selection of lags, number of nodes of the RBF network, lag orders and state vector, is encoded into a chromosome and is evolved simultaneously by a genetic algorithm (GA). This combines the advantages of the GA and the variable projection (VP) method to automatically generate a parsimonious RBF-ARX model with a high generalization performance. The highly efficient VP algorithm is used as a local search strategy to accelerate the convergence of the optimization. The experimental results demonstrate the effectiveness of the proposed method.

KeywordGenetic Algorithms Model Selection Parameter Estimation Rbf-arx Models Time Series Prediction Variable Projection
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Scopus ID2-s2.0-85127129677
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Document TypeJournal article
Corresponding AuthorChen, Guang Yong
Affiliation1.College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, China
2.Fujian Meteorological Information Center, Fuzhou, 350001, China
3.Faculty of Science and Technology, University of Macau, 99999, China
Recommended Citation
GB/T 7714
Chen, Qiong Ying,Chen, Long,Su, Jian Nan,et al. Model selection for RBF-ARX models[J]. Applied Soft Computing,2022,121.
APA Chen, Qiong Ying,Chen, Long,Su, Jian Nan,Fu, Ming Jian,&Chen, Guang Yong.(2022).Model selection for RBF-ARX models.Applied Soft Computing,121.
MLA Chen, Qiong Ying,et al."Model selection for RBF-ARX models".Applied Soft Computing 121(2022).
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