文件名称:anp
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NP是美国匹兹堡大学的T.L.Saaty 教授于1996年提出了一种适应非独立的递阶层次结构的决策方法,它是在网络分析法(AHP)基础上发展而形成的一种新的实用决策方法。其关键步骤有以下几个:
1 确定因素,并建立网络层和控制层模型。
2 创建比较矩阵。
3 按照指标类型针对每列进行规范化。
4 求出每个比较矩阵的最大特征值和对应的特征向量。
5 一致性检验。如果不满足,则调整相应的比较矩阵中的元素。
6 将各个特征向量单位化(归一化),组成判断矩阵。
7 将控制层的判断矩阵和网络层的判断矩阵相乘,得到加权超矩阵。
8 将加权超矩阵单位化(归一化),求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。
-NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following:
A determining factor, and a network layer and control layer model.
2 create a comparison matrix.
For each of the three types of indicators in accordance with normalized columns.
4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors.
5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements.
6 will each feature vector units of (normalized), to determine the composition of matrix.
7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix.
8 of the weighted super-matrix units of (normalized), seeking the powe
1 确定因素,并建立网络层和控制层模型。
2 创建比较矩阵。
3 按照指标类型针对每列进行规范化。
4 求出每个比较矩阵的最大特征值和对应的特征向量。
5 一致性检验。如果不满足,则调整相应的比较矩阵中的元素。
6 将各个特征向量单位化(归一化),组成判断矩阵。
7 将控制层的判断矩阵和网络层的判断矩阵相乘,得到加权超矩阵。
8 将加权超矩阵单位化(归一化),求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。
-NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following:
A determining factor, and a network layer and control layer model.
2 create a comparison matrix.
For each of the three types of indicators in accordance with normalized columns.
4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors.
5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements.
6 will each feature vector units of (normalized), to determine the composition of matrix.
7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix.
8 of the weighted super-matrix units of (normalized), seeking the powe
相关搜索: AHP
ANP
feature comparison in matlab
矩阵 排序
NP
ELECTRE matlab code
Analytic Network Process matlab code
Analytic network Process
MATLAB ahp
AHP MATLAB
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anp+matlab.txt
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