DCS-ELM: a novel method for extreme learning machine for regression problems and a new approach for the SFRSCC

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ALTAY O., Ulas M., Alyamac K. E.

PeerJ Computer Science, cilt.7, ss.1-31, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 7
  • Basım Tarihi: 2021
  • Doi Numarası: 10.7717/peerj-cs.411
  • Dergi Adı: PeerJ Computer Science
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1-31
  • Anahtar Kelimeler: Extreme learning machine, Discrete-time chaotic systems, Chaotic maps, Regression algorithm, SFRSCC
  • Manisa Celal Bayar Üniversitesi Adresli: Evet

Özet

Extreme learning machine (ELM) algorithm is widely used in regression and classification problems due to its advantages such as speed and high-performance rate. Different artificial intelligence-based optimization methods and chaotic systems have been proposed for the development of the ELM. However, a generalized solution method and success rate at the desired level could not be obtained. In this study, a new method is proposed as a result of developing the ELM algorithm used in regression problems with discrete-time chaotic systems. ELM algorithm has been improved by testing five different chaotic maps (Chebyshev, iterative, logistic, piecewise, tent) from chaotic systems. The proposed discrete-time chaotic systems based ELM (DCS-ELM) algorithm has been tested in steel fiber reinforced selfcompacting concrete data sets and public four different datasets, and a result of its performance compared with the basic ELM algorithm, linear regression, support vector regression, kernel ELM algorithm and weighted ELM algorithm. It has been observed that it gives a better performance than other algorithms.