Scopus
YÖKSİS DOI Eşleşti
SJR Q1
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
Doç. Dr. AHMET CEVAHİR ÇINAR →
YÖKSİS Kayıtları — ISSN Eşleşmesi
New WOA Variants for Superior Meta-heuristic Optimization with Multiple Hunter Whale Leading
2025 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi SEMA SERVİ →
A New Lifetime Model: Properties, Estimation, and Applications in Quality Control
2025 ISSN: 2193-567X SCI-Expanded Q2
Arş. Gör. ERDEM CANKUT →
Brain Tumor Detection with Transfer Learning Models Based on Attention Modules
2026 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi ZÜLEYHA YILMAZ ACAR →
Effect of Silica/Graphene Nanohybrid Particles on the Mechanical Properties of Epoxy Coatings
2019 ISSN: 2193-567X SCI-Expanded Q3
Doç. Dr. ŞAKİR YAZMAN →
Heuristic Optimization Based on Penalty Approach for Surface Permanent Magnet Synchronous Machines
2020 ISSN: 2193-567X SCI-Expanded Q1
Doç. Dr. MEHMET AKİF ŞAHMAN →
A Method of Classification Performance Improvement Via a Strategy of Clustering-Based Data Elimination Integrated with k-Fold Cross-Validation
2021 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi ONUR İNAN →
Training Feed-Forward Multi-Layer Perceptron Artificial Neural Networks with a Tree-Seed Algorithm
2020 ISSN: 2193-567X SCI-Expanded Q3
Doç. Dr. AHMET CEVAHİR ÇINAR →
Solving Multi‑Objective Resource Allocation Problem Using Multi‑Objective Binary Artificial Bee Colony Algorithm
2021 ISSN: 2193-567X SCI-Expanded Q3
Prof. Dr. FATİH BAŞÇİFTÇİ →
Theoretical and Experimental Investigation of the Performance of an Atkinson Cycle Engine
2021 ISSN: 2193-567X SCI-Expanded Q3
Dr. Öğr. Üyesi HALİL ERDİ GÜLCAN →
Optimization of Electricity Generation Parameters with Microbial Fuel Cell Using the Response Surface Method
2022 ISSN: 2193-567X SCI-Expanded Q2
Prof. Dr. ŞAKİR TAŞDEMİR →
The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning
2024 ISSN: 2193-567X SCI-Expanded Q2
Prof. Dr. MEHMET ÇUNKAŞ →
Enhanced Gain Dual-Port Compact Printed Meandered Log-Periodic Monopole Array Antenna Design with Octagonal-Ring Shaped FSS for Broadband 28 GHz Applications
2024 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi MEHMET YERLİKAYA →
The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning
2024 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi UĞUR TAŞKIRAN →
Determination of Temperature Effects on Cortical Bone Milling Using Taguchi Method
2025 ISSN: 2193-567X SCI-Expanded Q2
Prof. Dr. SÜLEYMAN NEŞELİ →
Characterizing Machining Indicators with Machine Learning Models Under Cellulose Nanocrystal and Graphene-Based Nanofluid Conditions
2025 ISSN: 2193-567X SCI-Expanded Q2
Doç. Dr. MUSTAFA KUNTOĞLU →
Makale Bilgileri
ISSN2193567X
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
Scimago Dergi (ISSN Eşleşmesi)
Arabian Journal for Science and Engineering
Q1
SJR Skoru0,521
H-Index81
ÜlkeGermany
Multidisciplinary (Q1)
Metrikler
62
Atıf
1
Yazar
6
Anahtar Kelime