期刊文献+

The order structure of fuzzy numbers based on the level characteristics and its application to optimization problems 被引量:3

The order structure of fuzzy numbers based on the level characteristics and its application to optimization problems
原文传递
导出
摘要 Ranking and comparing fuzzy numbers is an important part in many fuzzy optimization problems such as intelligent control and manufacturing system production line scheduling with uncertainty environments. In this paper, based on the level characteristic function and α-average of level cut sets of fuzzy number, we establish the IMα-metric method for measuring fuzzy number as a whole, and introduce the concept of IDα-difFerence that describes the reliability of IMα-metric value. Further, the basic properties and the separability of IMα-metric and IDα-difference are discussed. Finally, we give a mathematical model to solve fuzzy optimization problems by means of IMα-metric. Ranking and comparing fuzzy numbers is an important part in many fuzzy optimization problems such as intelligent control and manufacturing system production line scheduling with uncertainty environments. In this paper, based on the level characteristic function and α-average of level cut sets of fuzzy number, we establish the IMα-metric method for measuring fuzzy number as a whole, and introduce the concept of IDα-difFerence that describes the reliability of IMα-metric value. Further, the basic properties and the separability of IMα-metric and IDα-difference are discussed. Finally, we give a mathematical model to solve fuzzy optimization problems by means of IMα-metric.
出处 《Science in China(Series F)》 2002年第6期433-441,共9页 中国科学(F辑英文版)
基金 This work was supported by the National Natural Science Foundation of China (Grant No. 60004010) China Postdoctoral Science Foundation.
关键词 fuzzy numbers level characteristic function IMα-metric IDαdifference fuzzy optimization. fuzzy numbers, level characteristic function, IMα-metric, IDαdifference, fuzzy optimization.
  • 相关文献

参考文献18

  • 1[1]Goetschel, R., Voxman, W., Topological properties of fuzzy number, Fuzzy Sets and Systems, 1983, 10:87-99.
  • 2[2]Diamond, P., Kloeden, P., Characterization of compact subset of fuzzy sets, Fuzzy Sets and Systems, 1989,29:341-348.
  • 3[3]Diamond, P., Kloeden, P., Metric Space of Fuzzy Set: Theory and Applications, Singapore: World Scientific,1994.
  • 4[4]Wu Congxin, Li Fachao, Ha Minghu et al., Fuzzy metric and convergence based on the symmetric difference,Chinese Science Bulletin, 1998, 43(1): 106-107.
  • 5[5]Tanaka, H., Fuzzy data analysis by possibillistic linear models, Fuzzy Sets and Systems, 1987, 24: 363-375.
  • 6[6]Tong Shaocheng, Interval number and fuzzy number linear programming, Fuzzy Sets and Systems, 1994, 66:301-306.
  • 7[7]Kuwano, H., On the fuzzy multi-objective linear programming problem: Goal programming approach, Fuzzy Sets and Systems, 1996, 82: 57-64.
  • 8[8]Kassem, M. A. E., Ammar, E. I., Stability of multi-objective nonlinear programming problem with fuzzy parameters in the constraints, Fuzzy Sets and Systems, 1989, 29: 315-326.
  • 9[9]Lee, S., Olson, D., Comparison fuzzy number based on the probability measure of fuzzy events, Operation Research, 1988, 15: 887-896.
  • 10[10]Liou, T., Wang, M., Ranking fuzzy numbers with integral value, Fuzzy Sets and Systems, 1992, 50: 247-255.

同被引文献28

引证文献3

二级引证文献105

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部