Sparse basis pursuit on automatic nonlinear circuit modeling

In this paper, we propose a black-box nonlinear dynamic modeling algorithm that automatically selects essential basis functions to overcome the overfitting problem. Our automatic modeling algorithm, which is formulated as a convex optimization problem, guarantees model stability in transient simulat...

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Published inProceedings (International Conference on ASIC) pp. 1 - 4
Main Authors Yu-Chung Hsiao, Daniel, Luca
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2013
Subjects
Abstracts
Measurement uncertainty
Object recognition
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ISBN1467364150
9781467364157
ISSN2162-7541
DOI10.1109/ASICON.2013.6811858

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Abstract In this paper, we propose a black-box nonlinear dynamic modeling algorithm that automatically selects essential basis functions to overcome the overfitting problem. Our automatic modeling algorithm, which is formulated as a convex optimization problem, guarantees model stability in transient simulation. Furthermore, we incorporate our algorithm with a sparsity induction mechanism, which improves model robustness and generalization capabilities, as shown in our example.
AbstractList In this paper, we propose a black-box nonlinear dynamic modeling algorithm that automatically selects essential basis functions to overcome the overfitting problem. Our automatic modeling algorithm, which is formulated as a convex optimization problem, guarantees model stability in transient simulation. Furthermore, we incorporate our algorithm with a sparsity induction mechanism, which improves model robustness and generalization capabilities, as shown in our example.
Author Daniel, Luca
Yu-Chung Hsiao
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Snippet In this paper, we propose a black-box nonlinear dynamic modeling algorithm that automatically selects essential basis functions to overcome the overfitting...
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SubjectTerms Abstracts
Measurement uncertainty
Object recognition
Title Sparse basis pursuit on automatic nonlinear circuit modeling
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