电力科学与工程
電力科學與工程
전력과학여공정
INFORMATION ON ELECTRIC POWER
2015年
5期
11-15
,共5页
证据理论%信度函数%典型样本%概率分布
證據理論%信度函數%典型樣本%概率分佈
증거이론%신도함수%전형양본%개솔분포
evidence theory%typical sample%probability density%belief function
D-S证据理论信度函数分配的取值是得到较为准确的融合结果的关键，然而传统方法如采用隶属度函数、正态分布等得到的信度函数分配都具有较大的主观性。为使信度函数分配更具客观性，在总结其它方法的基础上，提出了基于典型样本的信度函数分配构造方法。首先采集各目标模式下的样本，并判断每一模式下的各条证据服从何种概率分布，利用相应的概率公式计算待识别目标模式的各条证据的概率密度，然后进行归一化处理，最后利用联合规则得到融合结果。实例表明利用此法可得到较为准确的融合结果，不仅提高了判别结果的准确性，而且降低了不确定度，并再一次证明了融合诊断结果比单一数据具有更高的可靠性。
D-S證據理論信度函數分配的取值是得到較為準確的融閤結果的關鍵，然而傳統方法如採用隸屬度函數、正態分佈等得到的信度函數分配都具有較大的主觀性。為使信度函數分配更具客觀性，在總結其它方法的基礎上，提齣瞭基于典型樣本的信度函數分配構造方法。首先採集各目標模式下的樣本，併判斷每一模式下的各條證據服從何種概率分佈，利用相應的概率公式計算待識彆目標模式的各條證據的概率密度，然後進行歸一化處理，最後利用聯閤規則得到融閤結果。實例錶明利用此法可得到較為準確的融閤結果，不僅提高瞭判彆結果的準確性，而且降低瞭不確定度，併再一次證明瞭融閤診斷結果比單一數據具有更高的可靠性。
D-S증거이론신도함수분배적취치시득도교위준학적융합결과적관건，연이전통방법여채용대속도함수、정태분포등득도적신도함수분배도구유교대적주관성。위사신도함수분배경구객관성，재총결기타방법적기출상，제출료기우전형양본적신도함수분배구조방법。수선채집각목표모식하적양본，병판단매일모식하적각조증거복종하충개솔분포，이용상응적개솔공식계산대식별목표모식적각조증거적개솔밀도，연후진행귀일화처리，최후이용연합규칙득도융합결과。실례표명이용차법가득도교위준학적융합결과，불부제고료판별결과적준학성，이차강저료불학정도，병재일차증명료융합진단결과비단일수거구유경고적가고성。
The value of D-S evidence theory of belief function assignment is the key to get accurate fusion results, while traditional methods, such as the belief function assignment got through the usage of the membership function and the normal distribution, tend to be more subjective.In order to make the belief function assignment more ob-jective.This paper puts forward a method constructing belief function assignment after combining other methods and analyzing the typical samples.First, samples were collected for each target mode, and which probability distribu-tions the evidence of each mode obeys was judged.Then, the probability density of each piece of evidence was cal-culated by using corresponding probability formula.Finally, normalized processing was carried on, and the union rule was used to obtain the fusion results.The example shows that using this method can get more accurate results of fusion, and this method can not only improves the accuracy of the result of discrimination, but also reduce the uncertainty.In addition, it proves that the fusion diagnosis has a higher reliability than a single data.
