Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24774
Title: Robust single hidden layer feed-forward neural networks modeling for small datasets
Authors: Zhang, R
Deng, ZH
Wang, ST
Choi, KS 
Qian, PJ
Keywords: ε-insensitive learning
Robustness
Single hidden layer feed-forward neural network
Structural risk minimization
Issue Date: 2012
Publisher: 《控制与决策》编辑委员会
Source: 控制与决策 (Control and decision), 2012, v. 27, no. 9, p. 1308-1312+1319 How to cite?
Journal: 控制与决策 (Control and decision) 
Abstract: Single hidden layer feed-forward neural network (SLFN) is one of the most widely used models for intelligent modeling. But the model faces that for small sample sets, the traditional learning algorithm may train a model to fall into the over-fitting sate. In particular, when the dataset contains a large amount of noise, the trained model has weak robustness and is very sensitive to noise. In order to overcome this shortcoming, a robust learning algorithm of SLFN is derived for small and noisy datasets. Due to the introduction of ε-insensitive learning measure and the structural risk term, the proposed algorithm can effectively overcome the shortcoming of the traditional learning algorithm. The experimental results on simulated and real-world datasets also confirm the above advantages.
URI: http://hdl.handle.net/10397/24774
ISSN: 1001-0920
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

49
Last Week
0
Last month
Checked on Oct 15, 2017

Google ScholarTM

Check



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.