MATEC Web Conf.
Volume 309, 20202019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
|Number of page(s)||7|
|Section||Smart Algorithms and Recognition|
|Published online||04 March 2020|
An efficient fuzzy optimization algorithm based on convolutional neural network
1 Hubei Collaborative Innovation Centre for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, 430068, China
2 School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
3 School of Industrial Design, Hubei University of Technology, Wuhan 430068, China
* Corresponding author: email@example.com
The paper proposes a method based on dense-sparse-dense optimization algorithm. It uses sparsity to tune network weights. By adding fuzzy membership, the optimization strategy can enhance the feature information with larger weights and weaken the feature information with less weight. Through accurate cutting of network weights, parameters in network are effectively reduced. The experimental results show that the performance of this method is better than the existing method.
Key words: Convolutional neural network / Optimization algorithm / Fuzzy membership / Network weights
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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