计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2013年
15期
234-237
,共4页
区间直觉模糊集%连续交叉熵%连续有序加权平均(COWA)算子%多属性决策
區間直覺模糊集%連續交扠熵%連續有序加權平均(COWA)算子%多屬性決策
구간직각모호집%련속교차적%련속유서가권평균(COWA)산자%다속성결책
interval-valued intuitionistic fuzzy sets%continuous cross-entropy%Continuous Ordered Weighted Average(COWA) operator%multi-criteria decision making
在区间直觉模糊(IVIF)环境下,利用连续有序加权平均(COWA)算子定义了一种新的区间直觉模糊数间的交叉熵,即区间直觉模糊连续交叉熵。依据提出的区间直觉模糊连续交叉熵定义了直觉模糊数间的连续交叉熵距离。基于TOPSIS的思想得到备选方案与理想方案的加权距离,并且计算备选方案与理想方案的相对贴近度,依据相对贴近度选择最优方案。其中,针对属性权重信息不完全确定条件下的决策问题,提出了以区间直觉模糊连续交叉熵最大为准则的规划模型;针对属性权重信息完全未知的情况,根据交叉熵理论确定属性权重向量。实验结果验证了新的决策方法的可行性和有效性。
在區間直覺模糊(IVIF)環境下,利用連續有序加權平均(COWA)算子定義瞭一種新的區間直覺模糊數間的交扠熵,即區間直覺模糊連續交扠熵。依據提齣的區間直覺模糊連續交扠熵定義瞭直覺模糊數間的連續交扠熵距離。基于TOPSIS的思想得到備選方案與理想方案的加權距離,併且計算備選方案與理想方案的相對貼近度,依據相對貼近度選擇最優方案。其中,針對屬性權重信息不完全確定條件下的決策問題,提齣瞭以區間直覺模糊連續交扠熵最大為準則的規劃模型;針對屬性權重信息完全未知的情況,根據交扠熵理論確定屬性權重嚮量。實驗結果驗證瞭新的決策方法的可行性和有效性。
재구간직각모호(IVIF)배경하,이용련속유서가권평균(COWA)산자정의료일충신적구간직각모호수간적교차적,즉구간직각모호련속교차적。의거제출적구간직각모호련속교차적정의료직각모호수간적련속교차적거리。기우TOPSIS적사상득도비선방안여이상방안적가권거리,병차계산비선방안여이상방안적상대첩근도,의거상대첩근도선택최우방안。기중,침대속성권중신식불완전학정조건하적결책문제,제출료이구간직각모호련속교차적최대위준칙적규화모형;침대속성권중신식완전미지적정황,근거교차적이론학정속성권중향량。실험결과험증료신적결책방법적가행성화유효성。
This paper presents the concept of the interval-valued intuitionistic fuzzy continuous cross-entropy under the interval-valued intuitionistic fuzzy environment, which is based on the COWA operator. The continuous cross-entropy distance between two interval-valued intuitionistic fuzzy values is proposed by using the concept of the interval-valued intuitionistic fuzzy contin-uous cross-entropy. It obtains the weighted distance degree values between every alternative and ideal alternative depending on the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)method, and calculates the relative closeness for each alternative with respect to ideal alternative. It can select the best alternative in accordance with the relative closeness. On the one hand, a programming model based on the principle of maximum cross-entropy is proposed to calculate the attribute weights aiming at the decision making problem with binding attribute weight conditions. On the other hand, it develops a method to obtain the attribute weights in accordance with the cross-entropy theory, aiming that the information about attribute weights is completely unknown. A practical example shows the feasibility and validity of the proposed decision making method.