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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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The Training Of Multiplicative Neuron Model Based Artificial Neural Networks With Differential Evolution Algorithm For Forecasting

Eren Bas
Published Online: 2016-01-13 | DOI: https://doi.org/10.1515/jaiscr-2016-0001


In recent years, artificial neural networks have been commonly used for time series forecasting by researchers from various fields. There are some types of artificial neural networks and feed forward artificial neural networks model is one of them. Although feed forward artificial neural networks gives successful forecasting results they have a basic problem. This problem is architecture selection problem. In order to eliminate this problem, Yadav et al. (2007) proposed multiplicative neuron model artificial neural network. In this study, differential evolution algorithm is proposed for the training of multiplicative neuron model for forecasting. The proposed method is applied to two well-known different real world time series data.

Keywords: Artificial neural networks; multiplicative neuron model; differential evolution algorithm; forecasting


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About the article

Published Online: 2016-01-13

Published in Print: 2016-01-01

Citation Information: Journal of Artificial Intelligence and Soft Computing Research, Volume 6, Issue 1, Pages 5–11, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2016-0001.

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© 2016 Academy of Management (SWSPiZ), Lodz. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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