Time series forecasting with feedforward neural networks trained using particle swarm optimizers for dynamic environments-Reference-Cited by-同舟云学术

Time series forecasting with feedforward neural networks trained using particle swarm optimizers for dynamic environments

Published:2020-07-07 Issue:7 Volume:33 Page:2667-2683
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Abdulkarim Salihu A.^ORCID,Engelbrecht Andries P.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-020-05163-4.pdf

Reference62 articles.

1. Abdulkarim S, Garko A (2016) Effectiveness of firefly algorithm based neural network in time series forecasting. Bayero J Pure Appl Sci 9(1):6–10

2. Abdulkarim SA (2018) Time series forecasting using dynamic particle swarm optimizer trained neural networks. Phd thesis, University of Pretoria

3. Abdulkarim SA, Engelbrecht AP (2019) Time series forecasting using neural networks: Are recurrent connections necessary? Neural Process Lett 50:2763–2795

4. Adhikari R, Agrawal RK (2011) Effectiveness of PSO based neural network for seasonal time series forecasting. In: Proceedings of the fifth Indian international conference on artificial intelligence, pp 231–244

5. Ahmadi MH, Aghaj SSG, Nazeri A (2013) Prediction of power in solar stirling heat engine by using neural network based on hybrid genetic algorithm and particle swarm optimization. Neural Comput Appl 22(6):1141–1150

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