PSO for training a regular Autoencoder.
Version 1.1.0 (2,16 MB) von
BERGHOUT Tarek
we used particle swarm optimization (PSO) for training an Autoencoder.
Particle swarm optimization is one the most well known based random search Algorithms in optimization.
In these codes and based on the references bellow, we introduce to you a fully connected regular autoencoder trained by PSO.
[1]ssM. N. Alam, “Particle Swarm Optimization : Algorithm and its Codes in MATLAB Particle Swarm Optimization : Algorithm and its Codes in MATLAB,” no. March, 2016.
[2]ssY. Liu, B. He, D. Dong, Y. Shen, and T. Yan, “ROS-ELM: A Robust Online Sequential Extreme Learning Machine for Big Data Analytics,” Proc. ELM-2014 Vol. 1, Algorthims Theor., vol. 3, pp. 325–344, 2015.
[3]ssH. Zhou, G.-B. Huang, Z. Lin, H. Wang, and Y. C. Soh, “Stacked Extreme Learning Machines.,” IEEE Trans. Cybern., vol. PP, no. 99, p. 1, 2014.
Zitieren als
BERGHOUT Tarek (2024). PSO for training a regular Autoencoder. (https://www.mathworks.com/matlabcentral/fileexchange/72388-pso-for-training-a-regular-autoencoder), MATLAB Central File Exchange. Abgerufen .
Kompatibilität der MATLAB-Version
Erstellt mit
R2013b
Kompatibel mit R2013b und späteren Versionen
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Loukmane
Loukmane/AE
Loukmane/NEW_PSO
Version | Veröffentlicht | Versionshinweise | |
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1.1.0 | Nothing changed - - just removed graphical abstract (image) |
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1.0.0 |