Numerical Simulation of Storage Tank Based on Control Theory of Fuzzy Rough Sets

Article Preview

Abstract:

In this paper we introduce the computer rough set theory and fuzzy control theory into the numerical simulation, and use the multimedia virtual simulation to do simulation on the heat transfer process of mechanical reserve tank, and establish 2D simulation model of tank, and add heat boundary condition in the oil tank bottom. Through the numerical simulation we obtain the curve of oil tank temperature and pressure with time. And through comparison of virtual computing results and experiment results, it verifies the reliability of the algorithm. At the end of this paper, we apply the multimedia virtual simulation technology to the basketball match, and analyze the technical parameters of the match. It provides the technical reference for the training of basketball players.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

3468-3471

Citation:

Online since:

March 2014

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Qian Yue, Shan Feng. System analysis of calculation performance for genetic algorithm [J]. Chinese Journal of computers, 2010, 32 (12): 2389-2392.

Google Scholar

[2] Junhua Li, Ming Li, Lihua Yuan. The pseudo parallel genetic algorithm based on clustering [J]. Pattern recognition and artificial intelligence, 2011, 22 (2): 188-194.

Google Scholar

[3] Jian Zhuang, Qingyu Yang, Haifeng Du, Dehong Yu. A high-performance complex system genetic algorithm [J]. Journal of software, 2010, 21 (11): 2790-2801.

DOI: 10.3724/sp.j.1001.2010.03673

Google Scholar

[4] Shouwen Chen, Shouwen Li. An improvement of genetic algorithm and its simulation [J]. Computer applications and software, 2010, 27 (9): 100-102.

Google Scholar

[5] Kang Wang, Xuesong Yan, Jian Jin, Zhigang. An improved clustering algorithm of genetic K means [J]. Computer and digital engineering, 2010, 38 (1): 18-20.

Google Scholar

[6] Biao Wang, Chanlun Duan, Hao Wu, Yonggang Song. Research and application of fuzzy set and rough set [D]. Beijing: Publishing House of electronics industry, 2011: 1-8.

Google Scholar

[7] Chen Lin. An Adaptive Genetic Algorithm based on Population Diversity Strategy [A]. New York: IEEE, 2009: 93-96.

Google Scholar

[8] Jun Liu. Application of an improved genetic algorithm in function optimization [D]. South China University of Technology, 2010: 12-16.

Google Scholar

[9] Xuesong Wang, Yang GAO, Yuhu Cheng, Ma Xiaoping. Robot path planning of knowledge guided genetic algorithm [J]. Control and decision, 2011, 7(24): 1043-1049.

Google Scholar

[10] Guoqiang Zhang, Xiaoming Peng. The improvement and application of adaptive genetic algorithm [J]. Ship Electronic Engineering, 2010(1): 58-65.

Google Scholar