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PSO for training a regular Autoencoder.

version 1.0.0 (2.08 MB) by BERGHOUT Tarek
we used particle swarm optimization (PSO) for training an Autoencoder.

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Updated 07 Aug 2019

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I dedicate this Work to my son.

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] M. N. Alam, “Particle Swarm Optimization : Algorithm and its Codes in MATLAB Particle Swarm Optimization : Algorithm and its Codes in MATLAB,” no. March, 2016.
[2] Y. 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] H. 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.

Cite As

BERGHOUT Tarek (2021). PSO for training a regular Autoencoder. (https://www.mathworks.com/matlabcentral/fileexchange/72388-pso-for-training-a-regular-autoencoder), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2013b
Compatible with R2013b and later releases
Platform Compatibility
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