...
首页> 外文期刊>Journal of Advanced Computatioanl Intelligence and Intelligent Informatics >Analysis of Temperature and q-Parameter Dependency of FCM with Tsallis Entropy Maximization
【24h】

Analysis of Temperature and q-Parameter Dependency of FCM with Tsallis Entropy Maximization

机译:TSALLIS熵最大化FCM的温度和Q参数依赖性分析

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

The Tsallis entropy is a q-parameter extension of the Shannon entropy. By maximizing it within the framework of fuzzy c-means, statistical mechanical membership functions can be derived. We propose a clustering algorithm that includes the membership function and deterministic annealing. One of the major issues for this method is the determination of an appropriate values for q and an initial annealing temperature for a given data distribution. Accordingly, in our previous study, we investigated the relationship between q and the annealing temperature. We quantitatively compared the area of the membership function for various values of q and for various temperatures. The results showed that the effect of q on the area was nearly the inverse of that of the temperature. In this paper, we analytically investigate this relationship by directly integrating the membership function, and the inversely proportional relationship between q and the temperature is approximately confirmed. Based on this relationship, a q-incrementation deterministic annealing fuzzy c-means (FCM) algorithm is developed. Experiments are performed, and it is confirmed that the algorithm works properly. However, it is also confirmed that differences in the shape of the membership function of the annealing method and that of the q-incrementation method are remained.
机译:Tsallis熵是Shannon熵的Q参数延伸。通过在模糊C型框架内最大化它,可以推导出统计机械隶属函数。我们提出了一种集群算法,包括隶属函数和确定性退火。该方法的主要问题之一是确定给定数据分布的Q和初始退火温度的适当值。因此,在我们以前的研究中,我们调查了Q和退火温度之间的关系。我们定量地将隶属函数的面积与各种温度的各种值进行了比较。结果表明,Q对该地区的效果几乎是温度的反比力。在本文中,我们通过直接整合隶属函数来分析这种关系,并且Q和温度之间的成反比关系大致证实。基于这种关系,开发了一种Q递增确定性退火模糊C-MATIOM(FCM)算法。进行实验,确认算法正常工作。然而,还证实了退火方法的成员函数的形状和Q递增方法的形状的差异。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号