IDEAS home Printed from https://ideas.repec.org/a/brc/journl/v31y2016i1p204-218.html
   My bibliography  Save this article

Analysis Of Sustainable Development Using Fuzzy Logic Prediction Models And Artifical Neural Networks

Author

Listed:
  • Daniel-Petru, GHENCEA

    (POLITEHNICA University of Bucharest)

  • Mihaela, ASANDEI

    („Constantin Brâncoveanu” University of Pitesti)

  • Miron, ZAPCIU

    (POLITEHNICA University of Bucharest)

Abstract

ustainable development is a priority of policies in countries all over the world, regardless of their level of development; this is a dynamic and complex concept based on indicators with vague and difficult to measure characteristics such as resources, labor, education, infrastructure, the existence of modern equipment to ensure manufacturing performance and flexibility. A model of approach and analysis of sustainable development using these indicators with vague characteristics can be achieved by combining prediction models: artificial neural networks and fuzzy logic. Artificial neural networks are used in the study, as they have the advantage of working with hidden layers, and recursive backpropagation algorithms to predict the size of indicators for a certain period, while fuzzy logic is used for three-dimensional interpretation of interdependencies and trends of indicators. The model provides long-term, flexible management decisions by eliminating bottlenecks and assessing deviations from a target defined so that the final result ensures a fast and flexible solution through fast and durable reconfiguring.

Suggested Citation

  • Daniel-Petru, GHENCEA & Mihaela, ASANDEI & Miron, ZAPCIU, 2016. "Analysis Of Sustainable Development Using Fuzzy Logic Prediction Models And Artifical Neural Networks," Management Strategies Journal, Constantin Brancoveanu University, vol. 31(1), pages 204-218.
    • Handle: RePEc:brc:journl:v:31:y:2016:i:1:p:204-218
    as

    Download full text from publisher

    File URL: http://www.strategiimanageriale.ro/papers/160130.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wenfei Luan & Ling Lu & Xin Li & Chunfeng Ma, 2018. "Integrating Extended Fourier Amplitude Sensitivity Test and Set Pair Analysis for Sustainable Development Evaluation from the View of Uncertainty Analysis," Sustainability, MDPI, vol. 10(7), pages 1-23, July.

    More about this item

    Keywords

    neural networks; fuzzy logic; sustainable development; management; decision-making; sustainability;
    All these keywords.

    JEL classification:

    • A12 - General Economics and Teaching - - General Economics - - - Relation of Economics to Other Disciplines
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D89 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Other

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:brc:journl:v:31:y:2016:i:1:p:204-218. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dan MICUDA (email available below). General contact details of provider: http://www.univcb.ro/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.