Dergiler / Turkish Journal of Electrical Engineering and Computer Science / 2004 / Cilt: 12 - Sayı: 1

Neural Analysis of Top Shielded Multilayered Coplanar Waveguides

Sayfa
1–10
Yayın Tarihi
2004-01-01
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

As a result, ANN models presented in this work can be used easily, simply and accurately to determine the characteristic parameters of the top shielded multilayered CPWs.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Artificial neural networks (ANNs) have been promising tools for many applications.
📊
Temel Bulgular & Veri:Better performance and learning speed with a simpler structure were achieved from these models.
💡
Sonuç & Katkı:As a result, ANN models presented in this work can be used easily, simply and accurately to determine the characteristic parameters of the top shielded multilayered CPWs.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Coplanar Waveguides · Effective Relative Permittivity · Characteristic Impedance

Özet

Artificial neural networks (ANNs) have been promising tools for many applications. In recent years, a computer-aided design approach based on (ANNs) has been introduced to microwave modelling, simulation and optimization. In this work, the characteristic parameters of top shielded multilayered coplanar waveguides (CPWs) have been determined with the use of ANN models. These neural models were trained with Levenberg-Marquardt, resilient propagation, Bayesian regulation, quasi-Newton, and backpropagation learning algorithms. Better performance and learning speed with a simpler structure were achieved from these models. The results have shown that the estimated characteristic parameters are in very good agreement with the computed results by using conformal mapping theory. The Levenberg-Marquardt learning algorithm was found to be the best algorithm among all. As a result, ANN models presented in this work can be used easily, simply and accurately to determine the characteristic parameters of the top shielded multilayered CPWs.

Abstract

Artificial neural networks (ANNs) have been promising tools for many applications. In recent years, a computer-aided design approach based on (ANNs) has been introduced to microwave modelling, simulation and optimization. In this work, the characteristic parameters of top shielded multilayered coplanar waveguides (CPWs) have been determined with the use of ANN models. These neural models were trained with Levenberg-Marquardt, resilient propagation, Bayesian regulation, quasi-Newton, and backpropagation learning algorithms. Better performance and learning speed with a simpler structure were achieved from these models. The results have shown that the estimated characteristic parameters are in very good agreement with the computed results by using conformal mapping theory. The Levenberg-Marquardt learning algorithm was found to be the best algorithm among all. As a result, ANN models presented in this work can be used easily, simply and accurately to determine the characteristic parameters of the top shielded multilayered CPWs.

🔖

Bu Makaleye Atıf Yap

TÜRKMEN, M., SAĞIROĞLU, C. Y. A. Ş. (2004). Neural Analysis of Top Shielded Multilayered Coplanar Waveguides. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 12 - Sayı: 1, 1–10. https://ajindex.com/articles/189070

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...