IDEAS home Printed from https://ideas.repec.org/a/ids/injdan/v4y2012i2p205-216.html
   My bibliography  Save this article

A comparative study on the performance of fuzzy logic, Bayesian logic and neural network towards decision-making

Author

Listed:
  • Dharmpal Singh
  • Jagannibas Paul Choudhury
  • Mallika De

Abstract

Soft computing models play an important role in the field of recognition, classification, data prediction, etc., and also in various application fields towards decision-making. Soft computing models include fuzzy logic, neural, network, genetic algorithm, particle swarm optimisation, tabu search, harmonie search, clustering, etc. The performance of a particular soft computing model can be ascertained using a particular dataset for the purpose of decision-making. Here, an effort has been made to make a comparison on the performance of fuzzy logic, Bayesian logic and neural network. The model with minimum error has been given preference for selection towards decision-making of information. The same method has been cross-checked based on the residual analysis to verify the earlier proposed observation. The said models have also been cross-checked based on other dataset. Under neural network, perceptron neural network model has been used.

Suggested Citation

  • Dharmpal Singh & Jagannibas Paul Choudhury & Mallika De, 2012. "A comparative study on the performance of fuzzy logic, Bayesian logic and neural network towards decision-making," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(2), pages 205-216.
    • Handle: RePEc:ids:injdan:v:4:y:2012:i:2:p:205-216
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=46792
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:injdan:v:4:y:2012:i:2:p:205-216. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=282 .

    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.