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

Author

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  • Oguzhan Yılmaz

    (Giresun University)

  • Eren Bas

    (Giresun University)

  • Erol Egrioglu

    (Giresun University)

Abstract

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.

Suggested Citation

  • Oguzhan Yılmaz & Eren Bas & Erol Egrioglu, 2022. "The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1699-1711, April.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:4:d:10.1007_s10614-020-10086-2
    DOI: 10.1007/s10614-020-10086-2
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    Cited by:

    1. Marius Gavrilescu & Sabina-Adriana Floria & Florin Leon & Silvia Curteanu, 2022. "A Hybrid Competitive Evolutionary Neural Network Optimization Algorithm for a Regression Problem in Chemical Engineering," Mathematics, MDPI, vol. 10(19), pages 1-29, September.

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