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Simulation With Artificial Intelligence To Forecast Gdp Depending On Logistics Elements

Author

Listed:
  • Margareta ILIE
  • Norina POPOVICI
  • Constantin ILIE

Abstract

The present paper as the main goal to establish a relatively new method to forecast gross domestic product depending on logistics elements using the artificial neural network simulation of the influence between the two types of data. In their research the authors determined the most appropriate form of neural network algorithm necessary for neural network training and they also analyzed the results obtained after training over the results of simulations. Also the authors intended to lay the foundations of using a non-linear methods that respond to the problems caused by rapid changes in the economic environment. The research result was the building a feedforward type of artificial neural network with a 4-5-1 structure. The training has been tested and evaluated, yielding values of errors comparison between actual and simulated data by the neural network less than 0.5%. The importance lies in the research results proving the efficiency of the method used in simulating the influence of elements of logistic activities over GDP values.

Suggested Citation

  • Margareta ILIE & Norina POPOVICI & Constantin ILIE, 2015. "Simulation With Artificial Intelligence To Forecast Gdp Depending On Logistics Elements," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 9(1), pages 1054-1061, November.
    • Handle: RePEc:rom:mancon:v:9:y:2015:i:1:p:1054-1061
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    References listed on IDEAS

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    1. G. M.P. Swann, 2009. "The Economics of Innovation," Books, Edward Elgar Publishing, number 13211.
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    Cited by:

    1. Constantin Ilie & Catalin Ploae & Lucia Violeta Melnic & Mirela Rodica Cotrumba & Andrei Marian Gurau & Coman Alexandra, 2019. "Sustainability through the Use of Modern Simulation Methods—Applied Artificial Intelligence," Sustainability, MDPI, vol. 11(8), pages 1-19, April.

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    Keywords

    neural network; GDP; simulation.;
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