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

Neural Analysis of Top Shielded Multilayered Coplanar Waveguides

Pages
1–10
Publication Date
2004-01-01
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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 Key Takeaways

🎯
Objective & Hypothesis:Artificial neural networks (ANNs) have been promising tools for many applications.
📊
Key Findings & Data:Better performance and learning speed with a simpler structure were achieved from these models.
💡
Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Coplanar Waveguides · Effective Relative Permittivity · Characteristic Impedance

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.

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.

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Cite This Article

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

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