A Modified Levenberg Marquardt Algorithm for Simultaneous Learning of Multiple Datasets

EFE M. Ö., Kurkcu B., Kasnakoglu C., Mohamed Z., Liu Z.

IEEE Transactions on Circuits and Systems II: Express Briefs, cilt.71, sa.4, ss.2379-2383, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 71 Sayı: 4
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1109/tcsii.2023.3335140
  • Dergi Adı: IEEE Transactions on Circuits and Systems II: Express Briefs
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.2379-2383
  • Anahtar Kelimeler: Levenberg-Marquardt algorithm, masked neural networks, multiple dataset learning
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Levenberg-Marquardt (LM) algorithm is a powerful approach to optimize the parameters of a neural network (NN). Given a training dataset, the algorithm synthesizes the best path toward the optimum. This paper demonstrates the use of LM optimization algorithm when there are more than one dataset and on/off type switching of NN parameters is allowed. For each dataset a pre-selected set of parameters are allowed for modification and the proposed scheme reformulates the Jacobian under the switching mechanism. The results show that a NN can store information available in different datasets by a simple modification to the original LM algorithm, which is the novelty introduced in this study. The results are verified on a regression problem.