International Journal of Computational Intelligence Systems

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Volume 9, Issue 6, December 2016, Pages 1191 - 1199

An Evaluation of the Dynamics of Diluted Neural Network

Authors
Lijuan Wang1, Jun Shen2, Qingguo Zhou3, *, Zhihao Shang3, Huaming Chen2, Hong Zhao4
1School of Cyber Engineering, Xidian University, Xi’an, China
2School of Computing and Information Technology, University of Wollongong, Wollongong, Australia
3School of Information Science and Engineering, Lanzhou University, Lanzhou, China
4Department of Physics, Xiamen University, Xiamen, China
* Corresponding author, E-mail:zhouqg@lzu.edu.cn
Corresponding Author
Qingguo Zhou
Received 5 January 2016, Accepted 2 August 2016, Available Online 1 December 2016.
DOI
10.1080/18756891.2016.1256578How to use a DOI?
Keywords
diluted neural network; annealed dilution; dynamics; spurious memory
Abstract

The Monte Carlo adaptation rule has been proposed to design asymmetric neural network. By adjusting the degree of the symmetry of the networks designed by this rule, the spurious memories or unwanted attractors of the networks can be suppressed completely. We have extended this rule to design full-connected neural networks and diluted neural networks. Comparing the dynamics of these two neural networks, the simulation results indicated that the performance of diluted neural network was poorer than the performance of full-connected neural network. As to this point, further research is needed. In this paper, we use the annealed dilution method to design a diluted neural network with fixed degree of dilution. By analyzing the dynamics of the diluted neural network, it is verified that asymmetric full-connected neural network do have significant advantages over the asymmetric diluted neural network.

Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 6
Pages
1191 - 1199
Publication Date
2016/12/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2016.1256578How to use a DOI?
Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Lijuan Wang
AU  - Jun Shen
AU  - Qingguo Zhou
AU  - Zhihao Shang
AU  - Huaming Chen
AU  - Hong Zhao
PY  - 2016
DA  - 2016/12/01
TI  - An Evaluation of the Dynamics of Diluted Neural Network
JO  - International Journal of Computational Intelligence Systems
SP  - 1191
EP  - 1199
VL  - 9
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1256578
DO  - 10.1080/18756891.2016.1256578
ID  - Wang2016
ER  -