DocumentCode
2043975
Title
Neural computing for building statistical macromodels of circuits and systems
Author
QingShan Zhou ; Yong Zou ; Qinghui Wu ; Minan Li ; Jiandong Hu
Author_Institution
Training Centre, Beijing Univ. of Posts & Telecommun., China
Volume
4
fYear
1993
fDate
19-21 Oct. 1993
Firstpage
261
Abstract
In this paper, the application of a backpropagation neural network in building statistical macromodels of circuits and systems is studied and the way to analyze the characteristics of the circuits with the help of the macromodel, a well trained BP network with the data measured from the circuit, is discussed. The neural network methodology is a novel way for circuit analysis, and as compared with statistical method, it´s much easier.<>
Keywords
backpropagation; circuit analysis computing; neural nets; backpropagation neural network; circuit analysis; circuits; neural computing; statistical macromodels; Analytical models; Circuit analysis; Circuit simulation; Circuits and systems; Design for experiments; Feedforward neural networks; Neural networks; Sensitivity analysis; Statistical analysis; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
TENCON '93. Proceedings. Computer, Communication, Control and Power Engineering.1993 IEEE Region 10 Conference on
Conference_Location
Beijing, China
Print_ISBN
0-7803-1233-3
Type
conf
DOI
10.1109/TENCON.1993.320482
Filename
320482
Link To Document