Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9530
Title: Fast learning algorithm to improve performance of Quickprop
Authors: Cheung, CC
Ng, SC
Issue Date: 2012
Publisher: Institution of Engineering and Technology
Source: Electronics letters, 2012, v. 48, no. 12, p. 678-679 How to cite?
Journal: Electronics letters 
Abstract: Quickprop is one of the most popular fast learning algorithms in training feed-forward neural networks. Its learning rate is fast; however, it is still limited by the gradient of the backpropagation algorithm and it is easily trapped into a local minimum. Proposed is a new fast learning algorithm to overcome these two drawbacks. The performance investigation in different learning problems (applications) shows that the new algorithm always converges with a faster learning rate compared with Quickprop and other fast learning algorithms. The improvement in global convergence capability is especially large, which increased from 4 to 100 in one learning problem.
URI: http://hdl.handle.net/10397/9530
ISSN: 0013-5194
EISSN: 1350-911X
DOI: 10.1049/el.2012.0947
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