PSO for training a regular Autoencoder.

Versione 1.1.0 (2,16 MB) da BERGHOUT Tarek
we used particle swarm optimization (PSO) for training an Autoencoder.
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Aggiornato 2 nov 2023

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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.

Cita come

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. Recuperato .

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Compatibile con R2013b e release successive
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Loukmane/NEW_PSO

Versione Pubblicato Note della release
1.1.0

Nothing changed - - just removed graphical abstract (image)
1.0.0