The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting

Yılmaz O., Bas E., Egrioglu E.

Computational Economics, vol.59, no.4, pp.1699-1711, 2022 (SCI-Expanded, SSCI, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 59 Issue: 4
  • Publication Date: 2022
  • Doi Number: 10.1007/s10614-020-10086-2
  • Journal Name: Computational Economics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, IBZ Online, International Bibliography of Social Sciences, ABI/INFORM, EconLit, INSPEC, zbMATH
  • Page Numbers: pp.1699-1711
  • Keywords: Pi-Sigma artificial neural networks, Differential Evolution Algorithm, Higher-order neural networks, Forecasting
  • Karadeniz Technical University Affiliated: No

Abstract

© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.Looking at the artificial neural networks’ literature, most of the studies started with feedforward artificial neural networks and the training of many feedforward artificial neural networks models are performed with derivative-based algorithms such as levenberg–marquardt and back-propagation learning algorithms in the first studies. In recent years, although many new heuristic algorithms have been proposed for different aims these heuristic algorithms are also frequently used in the training process of many different artificial neural network models. Pi-sigma artificial neural networks have different importance than many artificial neural network models with its higher-order network structure and superior forecasting performance. In this study, the training of Pi-Sigma artificial neural networks is performed by differential evolution algorithm uses DE/rand/1 mutation strategy. The performance of the proposed method is evaluated by two data sets and seen that the proposed method has a very effective performance compared with many artificial neural network models.