Strain Energy Prediction of Single Wall Carbon Nanotubes Using General Regression Neural Network and Adaptive Neuro–Fuzzy Inference System

Eyecioglu O., KAYIŞLI K., Beken M.

Electric Power Components and Systems, cilt.52, sa.8, ss.1437-1447, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 52 Sayı: 8
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1080/15325008.2023.2286346
  • Dergi Adı: Electric Power Components and Systems
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Environment Index, INSPEC, Metadex, DIALNET, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1437-1447
  • Anahtar Kelimeler: artificial neural networks, carbon nanotubes, computer simulation, fuzzy logic, strain energy
  • Gazi Üniversitesi Adresli: Evet

Özet

Single Wall Carbon Nanotubes (SWCNTs) play crucial roles in the field of nanotechnology research and applications. Due to their quantum mechanical nature and the intricate structure of SWCNTs, performing direct or indirect experimental processes can be exceedingly challenging. Simulation methods like Tight-Binding Molecular Dynamics (TBMD) can serve as viable alternatives to experimentation. However, it’s worth noting that these methods often demand extensive computational runtime. To address this computational time challenge, artificial intelligence algorithms such as the General Regression Neural Network (GRNN) and the Adaptive Neuro Fuzzy Interface System (ANFIS) have been proposed in this study. These models aim to calculate the energetic properties of SWCNTs more efficiently, offering practical and quicker predictive methods with reduced computational workloads. The study’s findings demonstrate a strong correlation between the predicted energy values of SWCNTs using GRNN and ANFIS models and the results obtained through TBMD simulations. Consequently, it is believed that these models can be suitable and effective approaches for computing the energetic properties of SWCNTs.