DocumentCode
2342779
Title
A new macromodeling approach for nonlinear microwave circuits based on recurrent neural networks
Author
Fang, Y.H. ; Yagoub, M.C.E. ; Wang, F. ; Zhang, Q.J.
Author_Institution
Dept. of Electron., Carleton Univ., Ottawa, Ont., Canada
Volume
2
fYear
2000
fDate
11-16 June 2000
Firstpage
883
Abstract
For the first time, recurrent neural networks (RNN) are trained to learn the dynamic responses of nonlinear microwave circuits. Once trained, the RNN macromodel provides fast prediction of the full analog behavior of the original circuit and can be used for high level simulation and optimization.
Keywords
analogue circuits; circuit optimisation; circuit simulation; microwave circuits; nonlinear network analysis; recurrent neural nets; RNN macromodel; dynamic responses; full analog behavior; high level optimization; high level simulation; macromodeling approach; nonlinear microwave circuits; recurrent neural networks; Circuit simulation; Circuit topology; Design optimization; Differential equations; Equivalent circuits; Microwave circuits; Microwave devices; Neural networks; Nonlinear circuits; Recurrent neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Microwave Symposium Digest. 2000 IEEE MTT-S International
Conference_Location
Boston, MA, USA
ISSN
0149-645X
Print_ISBN
0-7803-5687-X
Type
conf
DOI
10.1109/MWSYM.2000.863321
Filename
863321
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