DocumentCode
2353091
Title
A statistical perspective on nonlinear model reduction
Author
Phillips, Joel
Author_Institution
Cadence Berkeley Labs., San Jose, CA, USA
fYear
2003
fDate
7-8 Oct. 2003
Firstpage
41
Lastpage
46
Abstract
We consider the advantages of statistically motivated reasoning in analyzing the nonlinear model reduction problem. By adopting an information-theoretic analysis, we argue that the general analog macromodeling is tractable on average by nonlinear reduction methods. We provide examples to illustrate the importance of utilizing prior information, and provide a general outline of algorithms based on reproducing kernel Hilbert space machinery.
Keywords
Hilbert spaces; circuit simulation; information theory; nonlinear network synthesis; reduced order systems; statistical analysis; analog macromodeling; information-theoretic analysis; kernel Hilbert space machinery; nonlinear model reduction problem; statistically motivated reasoning; Circuit simulation; Circuit synthesis; Circuit testing; Computational modeling; Driver circuits; Hilbert space; Information analysis; Laboratories; Machinery; Reduced order systems;
fLanguage
English
Publisher
ieee
Conference_Titel
Behavioral Modeling and Simulation, 2003. BMAS 2003. Proceedings of the 2003 International Workshop on
Print_ISBN
0-7803-8135-1
Type
conf
DOI
10.1109/BMAS.2003.1249855
Filename
1249855
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