Scopus
YÖKSİS Eşleşti
Training Feed-Forward Multi-Layer Perceptron Artificial Neural Networks with a Tree-Seed Algorithm
Arabian Journal for Science and Engineering · Aralık 2020
YÖKSİS Kayıtları
Training Feed-Forward Multi-Layer Perceptron Artificial Neural Networks with a Tree-Seed Algorithm
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING · 2020 SCI-Expanded
DOÇENT AHMET CEVAHİR ÇINAR →
Makale Bilgileri
DergiArabian Journal for Science and Engineering
Yayın TarihiAralık 2020
Cilt / Sayfa45 · 10915-10938
Scopus ID2-s2.0-85090232496
Özet
The artificial neural network (ANN) is the most popular research area in neural computing. A multi-layer perceptron (MLP) is an ANN that has hidden layers. Feed-forward (FF) ANN is used for classification and regression commonly. Training of FF MLP ANN is performed by backpropagation (BP) algorithm generally. The main disadvantage of BP is trapping into local minima. Nature-inspired optimizers have some mechanisms escaping from the local minima. Tree-seed algorithm (TSA) is an effective population-based swarm intelligence algorithm. TSA mimics the relationship between trees and their seeds. The exploration and exploitation are controlled by search tendency which is a peculiar parameter of TSA. In this work, we train FF MLP ANN for the first time. TSA is compared with particle swarm optimization, gray wolf optimizer, genetic algorithm, ant colony optimization, evolution strategy, population-based incremental learning, artificial bee colony, and biogeography-based optimization. The experimental results show that TSA is the best in terms of mean classification rates and outperformed the opponents on 18 problems.
Yazarlar (1)
1
Ahmet Cevahir Cinar
Anahtar Kelimeler
Artificial neural network
Multi-layer perceptron
Nature inspired algorithms
Neural networks
Training neural network
Tree-seed algorithm
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
35
Atıf
1
Yazar
6
Anahtar Kelime