@inproceedings{2b40bffb77ad40319d55a5e040dc8cb5,
title = "Deterministic weight modification algorithm for efficient learning",
abstract = "This paper presents a new approach using deterministic weight modification (DWM) to speed up the convergence rate effectively and improve the global convergence capability of the standard and modified back-propagation (BP) algorithms. The main idea of DWM is to reduce the system error by changing the weights of a multi-layered feed-forward neural network in a deterministic way. Simulation results show that the performance of DWM is better than BP and other modified BP algorithms for a number of learning problems.",
author = "Ng, {S. C.} and Cheung, {C. C.} and Leung, {S. H.}",
year = "2004",
doi = "10.1109/IJCNN.2004.1380076",
language = "English",
isbn = "0780383591",
series = "IEEE International Conference on Neural Networks - Conference Proceedings",
pages = "1033--1038",
booktitle = "2004 IEEE International Joint Conference on Neural Networks - Proceedings",
note = "2004 IEEE International Joint Conference on Neural Networks - Proceedings ; Conference date: 25-07-2004 Through 29-07-2004",
}