A new statistical pattern recognition method and a new sequence hybrid method of intelligent systems

Matej Babič, Abdulhamit Subasi, Carlos Pérez Bergmann, Mahmoud Moradi, Noureddine Barka, Issam Abu-Mahfouz, Panin Sergey, Lanndon Ocampo, Brian J. Galli, Chengwu Zheng, Tomaž Vuherer, Ladislav Hluchy

Research output: Contribution to journalArticlepeer-review

Abstract

In the paper we use methods of the intelligent system to predict the complexity of the network fracture of hardened specimens. We use a mathematical method of the network theory and fractal geometry in engineering, particularly in laser techniques. Moreover, using the fractal geometry, we investigate the complexity of the network fracture of the robot-laser hardened specimens, and analyze specimens hardened with different robot laser-cell parameters, such as the speed and temperature. Laser hardening is a metal-surface treatment process complementary to the conventional and induction hardening process. In this paper, we present a new method for the statistical pattern recognition using statistical techniques in analyzing the data measurements in order to extract information and take oppromate decisions in particularly mechanical engineering. To predict of the complexity of the network fracture of hardened patterns, we use multiple regression, neural network and support vector machine and to predict topographical property of hardened specimens, we use a hybrid method of machine learning.
Original languageEnglish
Pages (from-to)110-116
Number of pages7
JournalElektrotehniski Vestnik/Electrotechnical Review
Volume86
Issue number3
Publication statusPublished - Mar 2019
Externally publishedYes

Keywords

  • Hybrid system of machine learning
  • Mechanical engineering
  • Pattern recognition

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