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: ijcte@iacsitp.com
    • Journal Metrics:

Editor-in-chief
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 2009 Vol.1(1): 97-101 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2009.V1.15

Modeling of Fluid Industry Based on Flexible Neural Tree

QU Shou-ning, LIU Zhao-lian, CUI Guang-qiang and FUAi-fang

Abstract—Realizing optimal control of fluid industry is a difficult problem due to its features of complexity, strong correlation, non-linear and uncertainty. For solving the problem, we propose build the whole model for it. Cement production process is taken as an instance, and its production process model is gotten by evolving flexible neural tree (FNT).The FNT model’s structure and parameters are optimized by probabilistic incremental program evolution (PIPE) and simulation annealing (SA) respectively. The result demonstrates that the put forward method is effective and feasible for solving the problem.

Index Terms—fluid industry, flexible neural tree, probabilistic incremental program evolution, simulation annealing

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Cite: QU Shou-ning, LIU Zhao-lian, CUI Guang-qiang and FUAi-fang, "Modeling of Fluid Industry Based on Flexible Neural Tree," International Journal of Computer Theory and Engineering vol. 1, no. 1, pp. 97-101, 2009.


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