Mixed gradient based fast learning algorithm with variable learning gain and selective updates for layered neural nets

Author

Xiang, Zengjun ; Bi, Guangguo

Author_Institution

Dept. of Radio Eng., Southeast Univ., Nanjing, China

fYear

1992

fDate

16-20 Nov 1992

Firstpage

1419

Abstract

A new fast adaptive learning algorithm is put forward, which uses the steepest descent method combined with the conjugate gradient method. Line search unconstrained optimization is adopted to adjust adaptively the learning gain. Computer simulation results are illustrated

Keywords

conjugate gradient methods; feedforward neural nets; learning (artificial intelligence); optimisation; conjugate gradient method; fast adaptive learning algorithm; layered neural nets; line search unconstrained optimisation; mixed gradient method; selective weight updates; steepest descent method; variable learning gain; Backpropagation algorithms; Bismuth; Computational complexity; Computer simulation; Convergence; Gain; Gradient methods; Neural networks; Nonhomogeneous media; Optimization methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Singapore ICCS/ISITA '92. 'Communications on the Move'

Print_ISBN

0-7803-0803-4

Type

conf

DOI

10.1109/ICCS.1992.255023

Filename

255023

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3431463