General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • Average Days from Submission to Acceptance: 192 days
    • E-mail:
    • Journal Metrics:

Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2011 Vol.3(1): 158-162 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2011.V3.299

An Improved Prediction Model based on Fuzzy-rough Set Neural Network

Jing Hong

Abstract—After the brief review of the basic principles and characteristics of BP (back-propagation) neural network and rough set theory, a novel artificial neural network model based on fuzzy-rough set is proposed in this paper, which is suitable for nonlinear regression to achieve the precise prediction result by introducing improved rough set to obtain minimal attribute set to solve the optimized problem of input layer. Compared to the normal prediction model, the feasibility and effectiveness of the proposed model is verified by a practical case study of the mine gas emission prediction.

Index Terms—fuzzy-rough set, neural network, prediction model


Cite: Jing Hong, "An Improved Prediction Model based on Fuzzy-rough Set Neural Network," International Journal of Computer Theory and Engineering vol. 3, no. 1, pp. 158-162, 2011.

Copyright © 2008-2024. International Association of Computer Science and Information Technology. All rights reserved.