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Results for Artificial neural networks

Publications & Outputs

  1. A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting

    Eğrioğlu, E. & Fildes, R., 20/11/2020, In: Computational Economics. 29 p.

    Research output: Contribution to journalJournal articlepeer-review

  2. A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks

    Bas, E., Egrioglu, E. & Yolcu, U., 13/04/2020, In: Journal of Ambient Intelligence and Humanized Computing. 9 p.

    Research output: Contribution to journalJournal articlepeer-review

  3. AR–ARCH Type Artificial Neural Network for Forecasting

    Corba, B. S., Egrioglu, E. & Dalar, A. Z., 1/02/2020, In: Neural Processing Letters. 51, p. 819–836 18 p.

    Research output: Contribution to journalJournal articlepeer-review

  4. Robust learning algorithm for multiplicative neuron model artificial neural networks

    Bas, E., Uslu, V. R. & Egrioglu, E., 1/09/2016, In: Expert Systems with Applications. 56, p. 80-88 9 p.

    Research output: Contribution to journalJournal articlepeer-review

  5. Recurrent Multiplicative Neuron Model Artificial Neural Network for Non-linear Time Series Forecasting

    Egrioglu, E., Yolcu, U., Aladag, C. H. & Bas, E., 1/04/2015, In: Neural Processing Letters. 41, 2, p. 249-258 10 p.

    Research output: Contribution to journalJournal articlepeer-review

  6. Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks

    Egrioglu, E., Aladag, C. H. & Yolcu, U., 15/02/2013, In: Expert Systems with Applications. 40, 3, p. 854-857 4 p.

    Research output: Contribution to journalJournal articlepeer-review

  7. A new linear & nonlinear artificial neural network model for time series forecasting

    Yolcu, U., Egrioglu, E. & Aladag, C. H., 1/01/2013, In: Decision Support Systems. 54, 3, p. 1340-1347 8 p.

    Research output: Contribution to journalJournal articlepeer-review

  8. New data mining and calibration approaches to the assessment of water treatment efficiency

    Bieroza, M., Baker, A. & Bridgeman, J., 02/2012, In: Advances in Engineering Software. 44, 1, p. 126-135 10 p.

    Research output: Contribution to journalJournal articlepeer-review

  9. Nonlinear forecasting with a hybrid approach combining SARIMA, ARCH and ANN

    Egrioglu, E., Aladag, C. H. & Kadilar, C., 1/12/2011, New Developments in Artificial Neural Networks Research. Nova Science Publishers, Inc., p. 221-228 8 p.

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

  10. Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

    Crone, S. F., Hibon, M. & Nikolopoulos, K., 07/2011, In: International Journal of Forecasting. 27, 3, p. 635-660 26 p.

    Research output: Contribution to journalJournal articlepeer-review

  11. Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks

    Alp Erilli, N., Yolcu, U., Eǧrioǧlu, E., Hakan Aladaǧ, Ĉ. & Öner, Y., 1/03/2011, In: Expert Systems with Applications. 38, 3, p. 2248-2252 5 p.

    Research output: Contribution to journalJournal articlepeer-review

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