Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (24): 50-52.DOI: 10.3778/j.issn.1002-8331.2009.24.016

• 研究、探讨 • Previous Articles     Next Articles

Genetic Fuzzy C-means algorithm adding in computing of RNA

LIN Chun,LI An-gui,LIU Qin-sheng   

  1. School of Applied Science,University of Science and Technology Beijing,Beijing 100083,China
  • Received:2008-05-13 Revised:2008-09-10 Online:2009-08-21 Published:2009-08-21
  • Contact: LIN Chun


林 春,李安贵,刘钦圣   

  1. 北京科技大学 应用科学学院 数学力学系,北京 100083
  • 通讯作者: 林 春

Abstract: The algorithm of FCM is applied extensively in fuzzy clustering analysis,but it has two disadvantages:The first one is that it can easily be trapped in a local optimum and also strongly depends on initialization,and the second one lies in its long time of computing a large number of data.The RNA computing which is based on the DNA computing is a new algorithm of intelligent optimum.To improve the ability of getting the global best solution and to increase the convergent speed,a genetic fuzzy cluster algorithm based on RNA computing(RNAGAFCM) is presented.The emulational experiment of RNAGAFCM shows that the new algorithm decreases the iterative times and increases the convergent speed.

Key words: Fuzzy C-means algorithm, RNA computing, genetic algorithm

摘要: 模糊C均值算法(FCM)在聚类分析中是目前比较流行和应用比较广泛的一种算法。但它存在两个弱点:一是对初始化非常敏感,容易陷入局部极值点;二是处理大数据集时耗时太长。基于RNA的分子计算是近年来新兴的一种智能优化计算方法。提出了基于RNA计算的遗传模糊聚类算法(RNAGAFCM),来提高收敛速度和全局寻优能力。仿真实验表明新算法比现有的遗传模糊聚类算法减少了迭代次数,提高了收敛速度。

关键词: 模糊C均值算法, RNA计算, 遗传算法

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