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Journals / Electrica / 2018 / Cilt: 18 - Sayı: 1

An Analysis of the Effects of SVM Parameters on the Dead-Time System Modeling

NİHAT KABAOĞLU
Rana ORTAÇ KABAOĞLU
Journal
Electrica
Pages
1–5
DOI
—
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AI Summary & Key Takeaways

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

The performance of the method proposed herein was examined with different parameters in linear and nonlinear dead-time systems.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Modeling a dead-time system is a common issue in engineering applications.
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Key Findings & Data:To address this issue, existing research has employed neural networks and fuzzy logic-based intelligent systems.
💡
Conclusion & Impact:The performance of the method proposed herein was examined with different parameters in linear and nonlinear dead-time systems.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Özet

Modeling a dead-time system is a common issue in engineering applications. To address this issue, existing research has employed neural networks and fuzzy logic-based intelligent systems. Herein, a dead-time system modeled with the aid of support vector machine regression, which has a good generalization feature, was investigated. The performance of the method proposed herein was examined with different parameters in linear and nonlinear dead-time systems.

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Cite This Article

KABAOĞLU, N., KABAOĞLU, R. O. (2018). An Analysis of the Effects of SVM Parameters on the Dead-Time System Modeling. Electrica, Cilt: 18 - Sayı: 1, 1–5. https://ajindex.com/articles/153724

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