Training multi-layer perceptron using harris hawks optimization
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Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, Harris hawks optimization (HHO) algorithm has been proposed as an up-to-date meta-heuristic algorithm for training multi-layer perceptron (MLP). The performance of the HHO-based MLP trainer was tested by employing five standard data sets (XOR, Balloon, Iris, Breast Cancer and Heart). The results were compared with those obtained with the sine cosine algorithm (SCA). Comparative statistical results showed that using HHO algorithm as a trainer is more effective and has a higher rate of classification ability. © 2020 IEEE.
Açıklama
2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2020 --26 June 2020 through 27 June 2020 -- -- 162106
2-s2.0-85089698640
2-s2.0-85089698640
Anahtar Kelimeler
Harris hawks optimization (HHO), Multi-layer perceptron (MLP), Sine cosine algorithm (SCA)
Kaynak
HORA 2020 - 2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
