摘要
在实际中用一个精确量很难描述一个比较模糊的概念 ,如好坏、高低等 ,而用模糊数学方法往往能很好地解决这类问题。在模糊评估和模糊控制中 ,常常碰到隶属度如何确定或将精确数据如何模糊化的问题。本文采用正态分布模型对概率型数据进行模糊化 ,并针对正态分布模型存在边缘非单调性的缺陷 ,对模型进行了改进 ,最终建立了改进型正态分布模糊化模型。通过模糊化实例计算 。
It is hard to explain the fuzzy conception by using the accurate parameters. But sometimes it is very easy to do so by using the fuzzy method. Membership is the key parameter in fuzziness, so how to confirm and calculate it is very important. In this paper a new fuzzyfication method which (bases) on normal distribution have been discussed and used. As there has been a no-monotone (problem) in the border in the normal fuzzyfication method, a new better method, called normal improvement (distribution) fuzzyfication method, has been adopted to solve the problem and to be finally used to (calculate) the membership depending on the given accurate probability data. This new method is (illustrated) to be efficient and feasible.
出处
《模糊系统与数学》
CSCD
2004年第4期59-63,共5页
Fuzzy Systems and Mathematics
关键词
模糊
模糊化
正态分布
Fuzziness
Fuzzyfication
Normal Distribution
