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The Single-to-Noise Ratio in Extreme Learning Machines

Nikola Sekulovic, MiloÃ…Â¡ Stojanovic, Aleksandra Panajotovic and MiloÃ…Â¡ Bandur
1College of Applied Technical Sciences, Aleksandra Medvedeva 20 18000 NiÃ…Â¡, Serbia., 2College of Applied Technical Sciences Aleksandra Medvedeva 20, 18000 NiÃ…Â¡, Serbia., 3Faculty of Electronic Engineering, University of NiÃ…Â¡ Aleksandra Medvedeva 14, 18000

Abstract: This work predicted the wireless channel environment on extreme learning machines. The environment we have selected has single output systems in microcellular and picocellualar characteristics. The performance indicators include the average squared error and time consumption for operations. In the experimentation process, we found that the signal-to-noise ratio values reflected less execution time and higher accuracy.

Keywords: Channel Prediction, Extreme Learning Machines, Microcellular Environment, Picocellular Environment The Single-to-Noise Ratio in Extreme Learning Machines

DOI:https://doi.org/10.6025/jdp/2023/13/2/51-57

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References:

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