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, vol.71, no.4, pp.2379-2383, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 71 Issue: 4
  • Publication Date: 2024
  • Doi Number: 10.1109/tcsii.2023.3335140
  • Journal Name: IEEE Transactions on Circuits and Systems II: Express Briefs
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.2379-2383
  • Keywords: Levenberg-Marquardt algorithm, masked neural networks, multiple dataset learning
  • Hacettepe University Affiliated: Yes


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.