DocumentCode
2064369
Title
Sparse basis pursuit on automatic nonlinear circuit modeling
Author
Yu-Chung Hsiao ; Daniel, Luca
Author_Institution
Electr. Eng. & Comput. Sci., Massachusetts Inst. of Technol., Cambridge, MA, USA
fYear
2013
fDate
28-31 Oct. 2013
Firstpage
1
Lastpage
4
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.
Keywords
convex programming; nonlinear dynamical systems; nonlinear network synthesis; automatic modeling algorithm; automatic nonlinear circuit modeling; basis functions; black-box nonlinear dynamic modeling algorithm; convex optimization problem; model stability; overfitting problem; sparse basis pursuit; sparsity induction mechanism; transient simulation; Abstracts; Measurement uncertainty; Object recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
ASIC (ASICON), 2013 IEEE 10th International Conference on
Conference_Location
Shenzhen
ISSN
2162-7541
Print_ISBN
978-1-4673-6415-7
Type
conf
DOI
10.1109/ASICON.2013.6811858
Filename
6811858
Link To Document