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

Nihat Kabaoğlu | Zendy; Rana Ortaç Kabaoğlu | Zendy

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

Author(s) -

Nihat Kabaoğlu,

Rana Ortaç Kabaoğlu

Publication year - 2018

Publication title -

istanbul university - journal of electrical and electronics engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.136

H-Index - 11

ISSN - 1303-0914

DOI - 10.5152/iujeee.2018.1801

Subject(s) - support vector machine , generalization , computer science , artificial neural network , fuzzy logic , nonlinear system , dead time , feature (linguistics) , artificial intelligence , machine learning , data mining , control engineering , engineering , mathematics , statistics , mathematical analysis , linguistics , physics , philosophy , quantum mechanics

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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