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Journals / Istanbul University Journal of Electrical and Electronics Engineering / 2011 / Cilt: 11 - Sayı: 1

A support vector regressıon method for reducıng the hıgh-order systems to fırst-order plus tıme-delay forms

Rana ORTAÇ KABADĞLU
Pages
1305–1309
Publication Date
2022-10-18
DOI
—
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AI Summary & Key Takeaways

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

Satisfactory performance is obtained at the simulations.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper, a novel method for reducing the high-order systems to first-order plus time-delay forms is introduced.
📊
Key Findings & Data:For this purpose Support Vector Machines, which became a popular learning algorithm, is employed.
💡
Conclusion & Impact:Satisfactory performance is obtained at the simulations.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this paper, a novel method for reducing the high-order systems to first-order plus time-delay forms is introduced. For this purpose Support Vector Machines, which became a popular learning algorithm, is employed. Three parameters of the first-order plus time-delay forms are estimated by three parallel support vector regression machines. Satisfactory performance is obtained at the simulations.

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Cite This Article

KABADĞLU, R. O. (2022). A support vector regressıon method for reducıng the hıgh-order systems to fırst-order plus tıme-delay forms. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 11 - Sayı: 1, 1305–1309. https://ajindex.com/articles/543697

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