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• W3005494819 abstract "In this paper, we consider the term selection problem for a class of separable nonlinear models. The strategy is a two-step process in which the nonlinear parameters of the model are first optimized by a variable projection method, and then the least absolute shrinkage and selection operator are adopted to obtain a sparse solution by picking out the critical terms automatically. This process may be repeated several times. The proposed algorithm is tested on parameter estimation problems for an exponential model and a neural network-based model. The numerical results show that the proposed algorithm can pick out the appropriate terms from the overparameterized model and the obtained parsimonious model performs better than other methods." @default.
• W3005494819 created "2020-02-14" @default.
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• W3005494819 date "2020-02-01" @default.
• W3005494819 modified "2023-10-18" @default.
• W3005494819 title "Term Selection for a Class of Separable Nonlinear Models" @default.
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• W3005494819 doi "https://doi.org/10.1109/tnnls.2019.2904952" @default.
• W3005494819 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30990193" @default.
• W3005494819 hasPublicationYear "2020" @default.
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