Speeding up the convergence of backpropagation networks

Autor:	S. Sureerattanan, Huynh Ngoc Phien
Rok vydání:	2002
Předmět:	Mathematical optimization Momentum (technical analysis) Artificial neural network Rate of convergence Computer science Convergence (routing) ComputerApplications_COMPUTERSINOTHERSYSTEMS Biological neuron model Kalman filter Algorithm Computer Science::Databases Backpropagation Term (time)
Zdroj:	IEEE. APCCAS 1998. 1998 IEEE Asia-Pacific Conference on Circuits and Systems. Microelectronics and Integrating Systems. Proceedings (Cat. No.98EX242).
DOI:	10.1109/apccas.1998.743905
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::59d74247fca83277c2c0d678d0dee346 https://doi.org/10.1109/apccas.1998.743905 Zobrazit plný text záznamu