水质评价中的冗余理论与去余算法
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摘要
水质评价在算法层面上解决的问题是,通过实现指标隶属度到目标隶属度转换确定目标水体的污染等级;存在的实质性问题是,如何界定隶属度转换不是简单的线性转换以及如何解决隶属度转换的非线性计算.为此,通过计算由隶属度向量表征的分类信息的信息熵、确定指标对水体分类所做贡献的大小;借助区分权概念揭示指标隶属度中包含对目标水体分类的冗余值.以"冗余"为切入点,建立以冗余定理、非线性转换等定理推论为基本内容的冗余理论.用冗余理论界定隶属度转换的非线性,建立基于区分权滤波的去余算法实现隶属度转换.所建模型在指标隶属度只取"0或1"两个数值时,将退化为通常的"加权平均"线性模型.
The paper points out that at the algorithm level water quality evaluation is to solve the problem of determining the class of pollution of the target of water by conversing from index membership degree to the objective membership degree.While the substantive issue is how to defy that the conversion of membership degree is not simple linear conversion and how to solve the nonlinear computation of conversion of membership degree.According to this problem,the paper suggests confirming the contribution the index made to water classification by calculating the information entropy of classification information characterized by the membership vector and revealing that the index membership degree contains redundancy value of the classification of the target of water by using the conception of distinguish right..Regard redundancy as cutting point,the paper aims at constructing redundancy theory with redundancy theory,nonlinear conversion and correlative theorem deduction as its main content.It defies the nonlinear of conversion of membership degree by redundancy theory and constructs redundancy removed algorithm basing on distinguish right to achieve conversion of membership degree.When the index membership degree takes only "0 or 1" two values,the model will degenerate into the usual linear model of the weighted average model.
The paper points out that at the algorithm level water quality evaluation is to solve the problem of determining the class of pollution of the target of water by conversing from index membership degree to the objective membership degree.While the substantive issue is how to defy that the conversion of membership degree is not simple linear conversion and how to solve the nonlinear computation of conversion of membership degree.According to this problem,the paper suggests confirming the contribution the index made to water classification by calculating the information entropy of classification information characterized by the membership vector and revealing that the index membership degree contains redundancy value of the classification of the target of water by using the conception of distinguish right..Regard redundancy as cutting point,the paper aims at constructing redundancy theory with redundancy theory,nonlinear conversion and correlative theorem deduction as its main content.It defies the nonlinear of conversion of membership degree by redundancy theory and constructs redundancy removed algorithm basing on distinguish right to achieve conversion of membership degree.When the index membership degree takes only "0 or 1" two values,the model will degenerate into the usual linear model of the weighted average model.
引文
[1]郭晓贝,齐童,李宏等.基于模糊综合评价法的北京是郊野公园水环境质量评价[J].首都师范大学学报(自然科学版),2012,33(1):57-60.
[2]梁淑轩,吴虹,李占臣等.环境空气质量动态变化规律的模糊数学综合评价[J].干旱区资源与环境,2010,24(6):77-81.
[3]邹海明,李粉茹.基于实例的水质模糊综合评价[J].安徽农学通报,2005,11(4):154-155.
[4]张春娜,李富平,郎东红.唐山市南湖公园水环境质量的模糊综合评价[J].安徽农业科学,2009,37(34):17000-17002、17006.
[5]姜莉莉,薛文平,孙辉,林海.模糊数学评价法在青龙河水质现状评价中的应用[J].大连轻工业学院学报,2007,26(1):56-59.
[6]周维博,郭小砾.塔里木河水质模糊模式识别评价[J].水资源保护,2007,23(4):33-36.
[7]沃飞,陈效民,吴华山等.灰色聚类法对太湖地区农村地下水水质的评价[J].安全域环境学报,2006,6(4):38-41.
[8]汤洁,李艳梅,卞建民等.物元可拓法在地下水水质评价中的应用[J].水文地质工程地质,2005(5):1-5.
[9]田景环,邱林,柴福鑫.模糊识别在水质评价中的应用[J].环境科学学报,2005,25(7):950-953.
[10]罗定贵,王学军,郭青.基于MATLAB实现的ANN方法在地下水质评价中的应用[J].北京大学学报(自然科学版),2004,42(2):296-302.
[11]陈守煜,李亚伟.基于模糊人工神经网络识别的水质评价模型[J].水科学进展,2005,16(1):88-91.
[12]汪新波,粟晓玲.基于T-S模糊神经网络的民勤地下水水质综合评价[J].干旱地区农业研究,2013,31(1):189-192、198.
[13]周忠涛.基于T-S模型的模糊神经网络在水质评价中的应用[D].南京:河海大学,2007.
[14]郑一华.基于支持向量机的水质评价和预测研究[D].南京:河海大学,2006.
[15]刘开第,张新,刘昕,庞彦军,金斓.水质评价涉及的因子权重与定权方法[J].数学的实践与认识,2014,45(2):175-183.
[16]刘勇,林奇,孟坤.一种基于信息熵的企业信息系统安全风险定量评估方法[J].计算机科学,2010,37(5):45-48.
[17]赵冬梅,马建峰,王跃生.信息系统的模糊风险评估模型[J].通信学报,2007,28(4):51-64.
[18]秦大力,张利,李吉慧.基于FAHP的信息安全风险评估方法[J].计算机工程,2009,3(15):156-158.
[2]梁淑轩,吴虹,李占臣等.环境空气质量动态变化规律的模糊数学综合评价[J].干旱区资源与环境,2010,24(6):77-81.
[3]邹海明,李粉茹.基于实例的水质模糊综合评价[J].安徽农学通报,2005,11(4):154-155.
[4]张春娜,李富平,郎东红.唐山市南湖公园水环境质量的模糊综合评价[J].安徽农业科学,2009,37(34):17000-17002、17006.
[5]姜莉莉,薛文平,孙辉,林海.模糊数学评价法在青龙河水质现状评价中的应用[J].大连轻工业学院学报,2007,26(1):56-59.
[6]周维博,郭小砾.塔里木河水质模糊模式识别评价[J].水资源保护,2007,23(4):33-36.
[7]沃飞,陈效民,吴华山等.灰色聚类法对太湖地区农村地下水水质的评价[J].安全域环境学报,2006,6(4):38-41.
[8]汤洁,李艳梅,卞建民等.物元可拓法在地下水水质评价中的应用[J].水文地质工程地质,2005(5):1-5.
[9]田景环,邱林,柴福鑫.模糊识别在水质评价中的应用[J].环境科学学报,2005,25(7):950-953.
[10]罗定贵,王学军,郭青.基于MATLAB实现的ANN方法在地下水质评价中的应用[J].北京大学学报(自然科学版),2004,42(2):296-302.
[11]陈守煜,李亚伟.基于模糊人工神经网络识别的水质评价模型[J].水科学进展,2005,16(1):88-91.
[12]汪新波,粟晓玲.基于T-S模糊神经网络的民勤地下水水质综合评价[J].干旱地区农业研究,2013,31(1):189-192、198.
[13]周忠涛.基于T-S模型的模糊神经网络在水质评价中的应用[D].南京:河海大学,2007.
[14]郑一华.基于支持向量机的水质评价和预测研究[D].南京:河海大学,2006.
[15]刘开第,张新,刘昕,庞彦军,金斓.水质评价涉及的因子权重与定权方法[J].数学的实践与认识,2014,45(2):175-183.
[16]刘勇,林奇,孟坤.一种基于信息熵的企业信息系统安全风险定量评估方法[J].计算机科学,2010,37(5):45-48.
[17]赵冬梅,马建峰,王跃生.信息系统的模糊风险评估模型[J].通信学报,2007,28(4):51-64.
[18]秦大力,张利,李吉慧.基于FAHP的信息安全风险评估方法[J].计算机工程,2009,3(15):156-158.