A new hybrid neural network classifier based on adaptive neuron and multiplicative neuron
Yazarlar (2)
Dr. Öğr. Üyesi Erdinç KOLAY Sinop Üniversitesi, Türkiye
Prof. Dr. Taner Tunç Ondokuz Mayıs Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Soft Computing (Q2) | ||
| Dergi ISSN | 1432-7643 Dergi Bilgileri (2023) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2023 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 06-08-2021 |
| Cilt / Sayı / Sayfa | 27 / 3 / 1797–1808 | DOI | 10.1007/s00500-021-06093-6 |
| Makale Linki | https://link.springer.com/10.1007/s00500-021-06093-6 | ||
| UAK Araştırma Alanları |
Yapay Zeka
Uygulamalı İstatistik
Yöneylem
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| Özet |
| Neural network (NN) classifiers are very popular tools for solving classification tasks. Mostly known NN classifier is a multilayer perceptron (MLP). Although MLP has a good correct classification ratio, its structure could be very complex and network training may work for a long time. Pi-sigma NN (PSNN) is higher-order NN (HONN), which used higher-order correlations among the input components to establish a HONN, and the PSNN utilizes the product of neurons as the output units. By contrast with MLP and PSNN, single multiplicative neuron (SMN) is simple concerning its structure and mathematical model. The absence of the hidden layer(s) could be an advantage for easy implementation, and the mathematical model can be easily interpreted. In this paper, we propose a new hybrid NN classifier based on simple adaptive neurons and SMN which form the SMN as a whole, where input units are constituted by … |
| Anahtar Kelimeler |
| Classification | Multilayer perceptron | Multiplicative neuron | Particle swarm optimization | Pi-sigma neural network |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 3 |
| Scopus | 4 |
| Google Scholar | 4 |