DocumentCode
2821572
Title
Dynamic and static numerical modeling of actual gradient-type neural networks
Author
Zurada, Jacek M. ; Kang, M.J.
Author_Institution
Dept. of Electr. Eng., Louisville Univ., KY, USA
fYear
1991
fDate
11-14 Jun 1991
Firstpage
2487
Abstract
A discussion is presented of the performance modeling of gradient-type networks utilizing continuous activation functions, finite input resistance of neurons, and other parasitic components within the neural system. Both time-domain performance and static numerical modeling of the networks are characterized and compared. It is shown that the relaxation algorithm may not guarantee the numerical convergence while the vector field method does
Keywords
convergence of numerical methods; modelling; neural nets; numerical methods; relaxation theory; time-domain analysis; continuous activation functions; finite input resistance; gradient-type; neural networks; neurons; numerical convergence; parasitic components; performance modeling; relaxation algorithm; static numerical modeling; time-domain performance; vector field method; Capacitance; Convergence; Electric resistance; Equations; Neural networks; Neurofeedback; Neurons; Numerical models; Time domain analysis; Voltage;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1991., IEEE International Sympoisum on
Print_ISBN
0-7803-0050-5
Type
conf
DOI
10.1109/ISCAS.1991.176031
Filename
176031
Link To Document