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

KABAOĞLU N., Kabaoglu R. O.

ELECTRICA, vol.18, no.1, pp.1-5, 2018 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 18 Issue: 1
  • Publication Date: 2018
  • Doi Number: 10.5152/iujeee.2018.1801
  • Journal Name: ELECTRICA
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.1-5
  • Istanbul University Affiliated: Yes


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.