DocumentCode
2536223
Title
Speeding up the convergence of backpropagation networks
Author
Sureerattanan, Songyot ; Phien, Huynh Ngoc
Author_Institution
Asian Inst. of Technol., Pathumthani, Thailand
fYear
1998
fDate
24-27 Nov 1998
Firstpage
651
Lastpage
654
Abstract
A new algorithm is proposed for speeding up the convergence of backpropagation (BP) networks. This algorithm is obtained by applying the momentum term and adaptive neuron model with temperature momentum term to the Kalman filter (KF) algorithm. It is found that this algorithm can perform satisfactorily in all cases considered. Not only the convergence rate can be improved, but also the sum of squared errors can be further reduced
Keywords
Kalman filters; backpropagation; convergence; neural nets; Kalman filter algorithm; adaptive neuron model; backpropagation networks; convergence; convergence rate; momentum term; squared errors sum; temperature momentum term; Backpropagation algorithms; Convergence; Electronic mail; Equations; Multi-layer neural network; Multilayer perceptrons; Neurons; Nonhomogeneous media; Supervised learning; Temperature;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1998. IEEE APCCAS 1998. The 1998 IEEE Asia-Pacific Conference on
Conference_Location
Chiangmai
Print_ISBN
0-7803-5146-0
Type
conf
DOI
10.1109/APCCAS.1998.743905
Filename
743905
Link To Document