Abstract
In this paper, we apply the Elman Neural Network (ENN) trained with Grey Wolf Optimizer (GWO) for time series predictions and data classification. The Grey Wolf Optimizer algorithm optimizes the network parameters. In order to evaluate the performance of the proposed method, we have carried out some experiments on two data sets: Mackey Glass, and Breast Cancer. We also give simulation examples to compare the effectiveness of the model with five known meta-heuristics methods in the literature. The results show that the GWO-ENN model produces a better generalization performance.
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Acknowledgments
The authors would like to acknowledge the financial support of this work by grants from the General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program01/UR/11/02.
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Rabhi, B., Dhahri, H., Alimi, A.M., Alturki, F.A. (2017). Grey Wolf Optimizer for Training Elman Neural Network. In: Abraham, A., Haqiq, A., Alimi, A., Mezzour, G., Rokbani, N., Muda, A. (eds) Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). HIS 2016. Advances in Intelligent Systems and Computing, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-319-52941-7_38
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DOI: https://doi.org/10.1007/978-3-319-52941-7_38
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