Properties and learning algorithms for faulty RBF networks with coexistence of weight and node failures-Reference-Cited by-同舟云学术

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Properties and learning algorithms for faulty RBF networks with coexistence of weight and node failures

Published:2017-02 Issue: Volume:224 Page:166-176
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Feng Rui-Bin,Han Zi-Fa,Wan Wai Yan,Leung Chi-Sing

Funder

Research Grants Council of the Government of the Hong Kong Special Administrative Region

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference44 articles.

1. Neuroscience;Yu;Nature,2016

2. G. Bolt, Fault models for artificial neural networks, in: Proceedings of IJCNN'1991, Singapore, 1991, pp. 1373–1378.

3. Evolving fault-tolerant neural networks;Zhou;Neural Comput. Appl.,2003

4. A fault-tolerant regularizer for RBF networks;Leung;IEEE Trans. Neural Netw.,2008

5. R. Martolia, A. Jain, L. Singla, Analysis and survey on fault tolerance in radial basis function networks, in: The 2015 International Conference on Computing, Communication Automation (ICCCA), 2015, pp. 469–473.

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