DocumentCode
288793
Title
Two adaptation methods of artificial neural networks
Author
Sun, Baocheng ; Zhang, Zhifang
Author_Institution
China Acad. of Electron. & Inf. Technol., Beijing, China
Volume
5
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
3211
Abstract
In order to cope with the existing errors in modeling of multilayered feedforward neural networks (MLF), this paper presents two adaptation methods of artificial neural networks: feedback adaptation and Taylor series expansion based adaptation, based on the trained MLF with some modeling errors. Simulation results show that the proposed two adaptation methods give good error-reduction in modeling and forecasting of MLF
Keywords
error analysis; feedback; feedforward neural nets; modelling; series (mathematics); Taylor series expansion based adaptation; error-reduction; feedback adaptation; forecasting; modeling errors; multilayered feedforward neural networks; Application software; Artificial neural networks; Computer errors; Feedforward neural networks; Multi-layer neural network; Neural networks; Neurofeedback; Neurons; Predictive models; Taylor series;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1901-X
Type
conf
DOI
10.1109/ICNN.1994.374749
Filename
374749
Link To Document