对抗择优抽取算法,双权重求出两列正向的综合评价值


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流程图


原始矩阵如下:


$$ \begin{array}{c|c|c|c|c|c|c}{M_{11 \times15}} & -PN1 & -PN2 & -PE1 & -PS1 & -PS2 &SN1 &SN2 &SN3 &SE1 &SS1 &RN1 &RN2 &RE1 &RE2 &RS1\\ \hline 2011 &0.654302103 &278.5577711 &0.104246565 &426.623418 &0.484969128 &0.518335321 &0.734225621 &0.050682504 &8.714200492 &8.950944846 &0.850812256 &10.3 &7380.279843 &35115.65191 &176.2\\ \hline 2012 &0.64018787 &230.2072698 &0.100574919 &430.0655108 &0.498760764 &0.524293616 &0.720190933 &0.053800771 &8.311141515 &8.547470721 &0.868804512 &11.1 &8725.395288 &39690.62085 &170.1\\ \hline 2013 &0.634839501 &232.7438152 &0.095228796 &433.091812 &0.510793577 &0.534174192 &0.725746269 &0.053359371 &8.162741259 &8.838568347 &0.889545989 &12.82 &10872.14779 &43857.04467 &144.4\\ \hline 2014 &0.621034465 &198.6498147 &0.094446274 &435.3089733 &0.524130018 &0.545060211 &0.730176133 &0.05345935 &8.324123649 &10.36566358 &0.912120266 &8.67 &12075.052 &47967.53527 &159.4\\ \hline 2015 &0.617366121 &179.1637029 &0.093725645 &438.6801925 &0.540016156 &0.547408526 &0.727055177 &0.052997358 &8.456326593 &10.10966409 &0.932741508 &50.29 &13182.83334 &51652.87565 &157.2\\ \hline 2016 &0.614656439 &134.5724311 &0.093564023 &442.1742592 &0.558923488 &0.547604836 &0.732802092 &0.052527861 &8.333882334 &10.73475734 &0.951164027 &50.54 &14328.14133 &55939.46599 &138.94\\ \hline 2017 &0.605388917 &93.61972547 &0.086820781 &445.1131496 &0.577785857 &0.548797995 &0.737336045 &0.052199574 &8.053402438 &11.01950072 &0.958207523 &62.2 &15557.73277 &61583.50207 &252.86\\ \hline 牛逼 &0.4 &100 &0.05 &400 &0.3 &0.7 &0.8 &0.1 &10 &20 &0.95 &12 &30000 &50000 &150\\ \hline 很好 &0.6 &200 &0.1 &600 &0.5 &0.5 &0.7 &0.08 &8 &15 &0.9 &9 &20000 &40000 &100\\ \hline 良 &0.7 &300 &0.2 &800 &0.6 &0.4 &0.6 &0.05 &6 &10 &0.8 &6 &15000 &30000 &50\\ \hline 很垃圾 &0.8 &400 &0.3 &1000 &0.7 &0.3 &0.5 &0.02 &4 &5 &0.7 &3 &10000 &20000 &20\\ \hline \end{array} $$

采用的归一方法如下


极差法

正向指标公式:$$ n_{ij} = \frac{{o_{ij}-min(o_{j})}}{{max(o_{j})-min(o_{j})}} $$

负向指标公式:$$ n_{ij} = \frac{max(o_{j})-{o_{ij}}}{{max(o_{j})-min(o_{j})}} $$


归一化矩阵如下


$$ \begin{array}{c|c|c|c|c|c|c}{M_{11 \times15}} & -PN1 & -PN2 & -PE1 & -PS1 & -PS2 &SN1 &SN2 &SN3 &SE1 &SS1 &RN1 &RN2 &RE1 &RE2 &RS1\\ \hline 2011 &0.364 &0.396 &0.783 &0.956 &0.538 &0.546 &0.781 &0.384 &0.786 &0.263 &0.584 &0.123 &0 &0.364 &0.671\\ \hline 2012 &0.4 &0.554 &0.798 &0.95 &0.503 &0.561 &0.734 &0.423 &0.719 &0.236 &0.654 &0.137 &0.059 &0.474 &0.645\\ \hline 2013 &0.413 &0.546 &0.819 &0.945 &0.473 &0.585 &0.752 &0.417 &0.694 &0.256 &0.734 &0.166 &0.154 &0.574 &0.534\\ \hline 2014 &0.447 &0.657 &0.822 &0.941 &0.44 &0.613 &0.767 &0.418 &0.721 &0.358 &0.822 &0.096 &0.208 &0.673 &0.599\\ \hline 2015 &0.457 &0.721 &0.825 &0.936 &0.4 &0.619 &0.757 &0.412 &0.743 &0.341 &0.901 &0.799 &0.257 &0.761 &0.589\\ \hline 2016 &0.463 &0.866 &0.826 &0.93 &0.353 &0.619 &0.776 &0.407 &0.722 &0.382 &0.973 &0.803 &0.307 &0.864 &0.511\\ \hline 2017 &0.487 &1 &0.853 &0.925 &0.306 &0.622 &0.791 &0.402 &0.676 &0.401 &1 &1 &0.362 &1 &1\\ \hline 牛逼 &1 &0.979 &1 &1 &1 &1 &1 &1 &1 &1 &0.968 &0.152 &1 &0.721 &0.558\\ \hline 很好 &0.5 &0.653 &0.8 &0.667 &0.5 &0.5 &0.667 &0.75 &0.667 &0.667 &0.775 &0.101 &0.558 &0.481 &0.344\\ \hline 良 &0.25 &0.326 &0.4 &0.333 &0.25 &0.25 &0.333 &0.375 &0.333 &0.333 &0.387 &0.051 &0.337 &0.24 &0.129\\ \hline 很垃圾 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0.116 &0 &0\\ \hline \end{array} $$
正极值点构成
$$ \begin{array}{c|c|c|c|c|c|c}{M_{1 \times15}} & -PN1 & -PN2 & -PE1 & -PS1 & -PS2 &SN1 &SN2 &SN3 &SE1 &SS1 &RN1 &RN2 &RE1 &RE2 &RS1\\ \hline \mathbf{Zone^+} &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1\\ \hline \end{array} $$
负极值点构成
$$ \begin{array}{c|c|c|c|c|c|c}{M_{1 \times15}} & -PN1 & -PN2 & -PE1 & -PS1 & -PS2 &SN1 &SN2 &SN3 &SE1 &SS1 &RN1 &RN2 &RE1 &RE2 &RS1\\ \hline \mathbf{Zone^-} &0 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0\\ \hline \end{array} $$

  采用的是熵权法(EWM)求权重W1

$$ \begin{array}{c|c|c|c|c|c|c}{M_{2 \times15}} & -PN1 & -PN2 & -PE1 & -PS1 & -PS2 &SN1 &SN2 &SN3 &SE1 &SS1 &RN1 &RN2 &RE1 &RE2 &RS1\\ \hline EWM所得权重 &0.056255 &0.052243 &0.040037 &0.04453 &0.05917 &0.048601 &0.042516 &0.056495 &0.042611 &0.074201 &0.044361 &0.196456 &0.123409 &0.057263 &0.061853\\ \hline 权重大小顺序 &8 &9 &15 &11 &5 &10 &14 &7 &13 &3 &12 &1 &2 &6 &4\\ \hline \end{array} $$

  采用的是CRITIC方法求权重W2

$$ \begin{array}{c|c|c|c|c|c|c}{M_{2 \times15}} & -PN1 & -PN2 & -PE1 & -PS1 & -PS2 &SN1 &SN2 &SN3 &SE1 &SS1 &RN1 &RN2 &RE1 &RE2 &RS1\\ \hline CRITIC方法所得权重 &0.053176 &0.066843 &0.066333 &0.077907 &0.058671 &0.060044 &0.065755 &0.060084 &0.063687 &0.062617 &0.072847 &0.088362 &0.067664 &0.070149 &0.06586\\ \hline 权重大小顺序 &15 &6 &7 &2 &14 &13 &9 &12 &10 &11 &3 &1 &5 &4 &8\\ \hline \end{array} $$

由两种权重方法,针对归一化矩阵分别求得评价值


切比雪夫 Chebyshev

$$CEV=\begin{array}{c|c|c|c|c|c|c}{M_{11 \times2}} &CEV1 &CEV2\\ \hline 2011 &0.0426 &0.0745\\ \hline 2012 &0.0423 &0.074\\ \hline 2013 &0.0421 &0.0736\\ \hline 2014 &0.0419 &0.0733\\ \hline 2015 &0.1569 &0.0729\\ \hline 2016 &0.1578 &0.0724\\ \hline 2017 &0.1965 &0.0884\\ \hline 牛逼 &0.1234 &0.0779\\ \hline 很好 &0.0689 &0.0564\\ \hline 良 &0.0416 &0.0282\\ \hline 很垃圾 &0.0143 &0.0078\\ \hline \end{array} $$

由妥协解公式求出基础决策矩阵(边界决策矩阵)


$$ Q_i = \left( 1-k \right) \left(\frac{ \sqrt {CEV1_i^2 - Min(CEV1_i)^2}} {\sqrt {Max(CEV1_i)^2 -Min(CEV1_i)^2}} \right) + k\left(\frac{\sqrt{ CEV2_i^2 - Min(CEV2_i)^2}}{\sqrt{Max(CEV2)^2 -Min(CEV2_i)^2}} \right) $$ $$base=\begin{array}{c|c|c|c|c|c|c}{M_{11 \times2}} &Q(k=0) &Q(k=1)\\ \hline 2011 &0.2046 &0.8412\\ \hline 2012 &0.2032 &0.8361\\ \hline 2013 &0.202 &0.8316\\ \hline 2014 &0.2011 &0.8283\\ \hline 2015 &0.7976 &0.8233\\ \hline 2016 &0.8019 &0.8181\\ \hline 2017 &1 &1\\ \hline 牛逼 &0.6256 &0.8807\\ \hline 很好 &0.3437 &0.6349\\ \hline 良 &0.1992 &0.3079\\ \hline 很垃圾 &0 &0\\ \hline \end{array} $$


AECM运算之一,获得交点(拐点)


求解线段在决策区间的交点,k代表决策系数

  所谓拐点,就是上述线段中的交点

  所谓排序分析,即每个决策系数k对应的Q值的优劣排序,数值越低越优。两个拐点之间要素的排序是稳定一致的

  拐点处(交点),存在着至少一次,某两个要素的排序是一致的。

  交点坐标位置接近,以至于观测不到交点,下面会变换坐标,使得拐点等距,这样方便观测拐点具体的值。

  由上图得到交点加上k=0,k=1即得到所有拐点,结果如下。

$$\begin{array}{c|c|c|c|c|c|c}{M_{17 \times1}} &拐点对应的k值\\ \hline 0 &0\\ \hline 1 &0.4028\\ \hline 2 &0.4113\\ \hline 3 &0.4188\\ \hline 4 &0.4245\\ \hline 5 &0.4506\\ \hline 6 &0.738\\ \hline 7 &0.7499\\ \hline 8 &0.9628\\ \hline 9 &0.9707\\ \hline 10 &0.9709\\ \hline 11 &0.978\\ \hline 12 &0.9789\\ \hline 13 &0.9833\\ \hline 14 &0.9863\\ \hline 15 &0.9917\\ \hline 16 &1\\ \hline \end{array} $$

AECM运算之二,排序聚类分析


$$Qk_{matrix}=\begin{array}{c|c|c|c|c|c|c}{M_{11 \times17}} &k=0 &k=0.403 &k=0.411 &k=0.419 &k=0.424 &k=0.451 &k=0.738 &k=0.75 &k=0.963 &k=0.971 &k=0.971 &k=0.978 &k=0.979 &k=0.983 &k=0.986 &k=0.992 &k=1\\ \hline 2011 &0.205 &0.461 &0.466 &0.471 &0.475 &0.491 &0.674 &0.682 &0.818 &0.823 &0.823 &0.827 &0.828 &0.831 &0.832 &0.836 &0.841\\ \hline 2012 &0.203 &0.458 &0.463 &0.468 &0.472 &0.488 &0.67 &0.678 &0.813 &0.818 &0.818 &0.822 &0.823 &0.826 &0.827 &0.831 &0.836\\ \hline 2013 &0.202 &0.456 &0.461 &0.466 &0.469 &0.486 &0.667 &0.674 &0.808 &0.813 &0.813 &0.818 &0.818 &0.821 &0.823 &0.826 &0.832\\ \hline 2014 &0.201 &0.454 &0.459 &0.464 &0.467 &0.484 &0.664 &0.671 &0.805 &0.81 &0.81 &0.815 &0.815 &0.818 &0.82 &0.823 &0.828\\ \hline 2015 &0.798 &0.808 &0.808 &0.808 &0.809 &0.809 &0.817 &0.817 &0.822 &0.823 &0.823 &0.823 &0.823 &0.823 &0.823 &0.823 &0.823\\ \hline 2016 &0.802 &0.808 &0.809 &0.809 &0.809 &0.809 &0.814 &0.814 &0.818 &0.818 &0.818 &0.818 &0.818 &0.818 &0.818 &0.818 &0.818\\ \hline 2017 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1\\ \hline 牛逼 &0.626 &0.728 &0.731 &0.732 &0.734 &0.741 &0.814 &0.817 &0.871 &0.873 &0.873 &0.875 &0.875 &0.876 &0.877 &0.879 &0.881\\ \hline 很好 &0.344 &0.461 &0.463 &0.466 &0.467 &0.475 &0.559 &0.562 &0.624 &0.626 &0.626 &0.628 &0.629 &0.63 &0.631 &0.632 &0.635\\ \hline 良 &0.199 &0.243 &0.244 &0.245 &0.245 &0.248 &0.279 &0.281 &0.304 &0.305 &0.305 &0.306 &0.306 &0.306 &0.306 &0.307 &0.308\\ \hline 很垃圾 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0 &0\\ \hline \end{array} $$

    上述两列都是正向指标,数值越大越好。因此排序情况如下:

$$Q_{rank}=\begin{array}{c|c|c|c|c|c|c}{M_{11 \times17}} &k=0 &k=0.403 &k=0.411 &k=0.419 &k=0.424 &k=0.451 &k=0.738 &k=0.75 &k=0.963 &k=0.971 &k=0.971 &k=0.978 &k=0.979 &k=0.983 &k=0.986 &k=0.992 &k=1\\ \hline 2011 &6 &6 &5 &5 &5 &5 &5 &5 &5 &4 &3 &3 &3 &3 &3 &3 &3\\ \hline 2012 &7 &7 &7 &6 &6 &6 &6 &6 &6 &6 &6 &5 &5 &4 &4 &4 &4\\ \hline 2013 &8 &8 &8 &8 &7 &7 &7 &7 &7 &7 &7 &7 &6 &6 &6 &5 &5\\ \hline 2014 &9 &9 &9 &9 &9 &8 &8 &8 &8 &8 &8 &8 &8 &8 &7 &7 &6\\ \hline 2015 &3 &3 &3 &3 &3 &3 &2 &3 &3 &4 &4 &4 &5 &5 &6 &7 &7\\ \hline 2016 &2 &2 &2 &2 &2 &3 &4 &4 &5 &5 &6 &7 &7 &8 &8 &8 &8\\ \hline 2017 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1 &1\\ \hline 牛逼 &4 &4 &4 &4 &4 &4 &4 &3 &2 &2 &2 &2 &2 &2 &2 &2 &2\\ \hline 很好 &5 &6 &7 &8 &9 &9 &9 &9 &9 &9 &9 &9 &9 &9 &9 &9 &9\\ \hline 良 &10 &10 &10 &10 &10 &10 &10 &10 &10 &10 &10 &10 &10 &10 &10 &10 &10\\ \hline 很垃圾 &11 &11 &11 &11 &11 &11 &11 &11 &11 &11 &11 &11 &11 &11 &11 &11 &11\\ \hline \end{array} $$

   拐点与区段的排序如下:其中拐点中交点的位置有相等的情况出现。

序号性质与对应k值 区段大小 Q值排序
100$2017\succ 2016\succ 2015\succ 牛逼\succ 很好\succ 2011\succ 2012\succ 2013\succ 2014\succ 良\succ 很垃圾$
20<$k$<0.4028350.402835$2017\succ 2016\succ 2015\succ 牛逼\succ 很好\succ 2011\succ 2012\succ 2013\succ 2014\succ 良\succ 很垃圾$
30.4028350$2017\succ 2016\succ 2015\succ 牛逼\succ 2011\succ 很好 = 2012\succ 2013\succ 2014\succ 良\succ 很垃圾$
40.402835<$k$<0.4112730.008439$2017\succ 2016\succ 2015\succ 牛逼\succ 2011\succ 很好\succ 2012\succ 2013\succ 2014\succ 良\succ 很垃圾$
50.4112730$2017\succ 2016\succ 2015\succ 牛逼\succ 2011\succ 2012\succ 很好 = 2013\succ 2014\succ 良\succ 很垃圾$
60.411273<$k$<0.4188480.007574$2017\succ 2016\succ 2015\succ 牛逼\succ 2011\succ 2012\succ 很好\succ 2013\succ 2014\succ 良\succ 很垃圾$
70.4188480$2017\succ 2016\succ 2015\succ 牛逼\succ 2011\succ 2012\succ 2013\succ 很好 = 2014\succ 良\succ 很垃圾$
80.418848<$k$<0.4244920.005644$2017\succ 2016\succ 2015\succ 牛逼\succ 2011\succ 2012\succ 2013\succ 很好\succ 2014\succ 良\succ 很垃圾$
90.4244920$2017\succ 2016\succ 2015\succ 牛逼\succ 2011\succ 2012\succ 2013\succ 很好\succ 2014 = 良\succ 很垃圾$
100.424492<$k$<0.4505530.026061$2017\succ 2016\succ 2015\succ 牛逼\succ 2011\succ 2012\succ 2013\succ 2014\succ 很好\succ 良\succ 很垃圾$
110.4505530$2017\succ 2015\succ 2016 = 牛逼\succ 2011\succ 2012\succ 2013\succ 2014\succ 很好\succ 良\succ 很垃圾$
120.450553<$k$<0.7380390.287487$2017\succ 2015\succ 2016\succ 牛逼\succ 2011\succ 2012\succ 2013\succ 2014\succ 很好\succ 良\succ 很垃圾$
130.7380390$2017\succ 2015\succ 牛逼\succ 2016 = 2011\succ 2012\succ 2013\succ 2014\succ 很好\succ 良\succ 很垃圾$
140.738039<$k$<0.7498670.011827$2017\succ 2015\succ 牛逼\succ 2016\succ 2011\succ 2012\succ 2013\succ 2014\succ 很好\succ 良\succ 很垃圾$
150.7498670$2017\succ 牛逼\succ 2015 = 2016\succ 2011\succ 2012\succ 2013\succ 2014\succ 很好\succ 良\succ 很垃圾$
160.749867<$k$<0.9628060.21294$2017\succ 牛逼\succ 2015\succ 2016\succ 2011\succ 2012\succ 2013\succ 2014\succ 很好\succ 良\succ 很垃圾$
170.9628060$2017\succ 牛逼\succ 2015\succ 2011\succ 2016 = 2012\succ 2013\succ 2014\succ 很好\succ 良\succ 很垃圾$
180.962806<$k$<0.9707190.007913$2017\succ 牛逼\succ 2015\succ 2011\succ 2016\succ 2012\succ 2013\succ 2014\succ 很好\succ 良\succ 很垃圾$
190.9707190$2017\succ 牛逼\succ 2011\succ 2015 = 2016\succ 2012\succ 2013\succ 2014\succ 很好\succ 良\succ 很垃圾$
200.970719<$k$<0.9708630.000144$2017\succ 牛逼\succ 2011\succ 2015\succ 2016\succ 2012\succ 2013\succ 2014\succ 很好\succ 良\succ 很垃圾$
210.9708630$2017\succ 牛逼\succ 2011\succ 2015\succ 2012\succ 2016 = 2013\succ 2014\succ 很好\succ 良\succ 很垃圾$
220.970863<$k$<0.9780280.007165$2017\succ 牛逼\succ 2011\succ 2015\succ 2012\succ 2016\succ 2013\succ 2014\succ 很好\succ 良\succ 很垃圾$
230.9780280$2017\succ 牛逼\succ 2011\succ 2015\succ 2012\succ 2016\succ 2013 = 2014\succ 很好\succ 良\succ 很垃圾$
240.978028<$k$<0.9789490.000921$2017\succ 牛逼\succ 2011\succ 2015\succ 2012\succ 2013\succ 2016\succ 2014\succ 很好\succ 良\succ 很垃圾$
250.9789490$2017\succ 牛逼\succ 2011\succ 2015\succ 2012 = 2013\succ 2016\succ 2014\succ 很好\succ 良\succ 很垃圾$
260.978949<$k$<0.9833260.004377$2017\succ 牛逼\succ 2011\succ 2012\succ 2015\succ 2013\succ 2016\succ 2014\succ 很好\succ 良\succ 很垃圾$
270.9833260$2017\succ 牛逼\succ 2011\succ 2012\succ 2015\succ 2013\succ 2016\succ 2014 = 很好\succ 良\succ 很垃圾$
280.983326<$k$<0.986270.002944$2017\succ 牛逼\succ 2011\succ 2012\succ 2015\succ 2013\succ 2014\succ 2016\succ 很好\succ 良\succ 很垃圾$
290.986270$2017\succ 牛逼\succ 2011\succ 2012\succ 2013\succ 2015 = 2014\succ 2016\succ 很好\succ 良\succ 很垃圾$
300.98627<$k$<0.9916840.005414$2017\succ 牛逼\succ 2011\succ 2012\succ 2013\succ 2015\succ 2014\succ 2016\succ 很好\succ 良\succ 很垃圾$
310.9916840$2017\succ 牛逼\succ 2011\succ 2012\succ 2013\succ 2014\succ 2015 = 2016\succ 很好\succ 良\succ 很垃圾$
320.991684<$k$<10.008316$2017\succ 牛逼\succ 2011\succ 2012\succ 2013\succ 2014\succ 2015\succ 2016\succ 很好\succ 良\succ 很垃圾$
3310$2017\succ 牛逼\succ 2011\succ 2012\succ 2013\succ 2014\succ 2015\succ 2016\succ 很好\succ 良\succ 很垃圾$

   提取区段的位置

序号 聚类特征-对应k值区段 区段大小 Q值排序
10<$k$< 0.4028350.402835$2017 \succ 2016 \succ 2015 \succ 牛逼 \succ 很好 \succ 2011 \succ 2012 \succ 2013 \succ 2014 \succ 良 \succ 很垃圾$
20.402835<$k$< 0.4112730.008438$2017 \succ 2016 \succ 2015 \succ 牛逼 \succ 2011 \succ 很好 \succ 2012 \succ 2013 \succ 2014 \succ 良 \succ 很垃圾$
30.411273<$k$< 0.4188480.007575$2017 \succ 2016 \succ 2015 \succ 牛逼 \succ 2011 \succ 2012 \succ 很好 \succ 2013 \succ 2014 \succ 良 \succ 很垃圾$
40.418848<$k$< 0.4244920.005644$2017 \succ 2016 \succ 2015 \succ 牛逼 \succ 2011 \succ 2012 \succ 2013 \succ 很好 \succ 2014 \succ 良 \succ 很垃圾$
50.424492<$k$< 0.4505530.026061$2017 \succ 2016 \succ 2015 \succ 牛逼 \succ 2011 \succ 2012 \succ 2013 \succ 2014 \succ 很好 \succ 良 \succ 很垃圾$
60.450553<$k$< 0.7380390.287486$2017 \succ 2015 \succ 2016 \succ 牛逼 \succ 2011 \succ 2012 \succ 2013 \succ 2014 \succ 很好 \succ 良 \succ 很垃圾$
70.738039<$k$< 0.7498670.011828$2017 \succ 2015 \succ 牛逼 \succ 2016 \succ 2011 \succ 2012 \succ 2013 \succ 2014 \succ 很好 \succ 良 \succ 很垃圾$
80.749867<$k$< 0.9628060.212939$2017 \succ 牛逼 \succ 2015 \succ 2016 \succ 2011 \succ 2012 \succ 2013 \succ 2014 \succ 很好 \succ 良 \succ 很垃圾$
90.962806<$k$< 0.9707190.007913$2017 \succ 牛逼 \succ 2015 \succ 2011 \succ 2016 \succ 2012 \succ 2013 \succ 2014 \succ 很好 \succ 良 \succ 很垃圾$
100.970719<$k$< 0.9708630.000144$2017 \succ 牛逼 \succ 2011 \succ 2015 \succ 2016 \succ 2012 \succ 2013 \succ 2014 \succ 很好 \succ 良 \succ 很垃圾$
110.970863<$k$< 0.9780280.007165$2017 \succ 牛逼 \succ 2011 \succ 2015 \succ 2012 \succ 2016 \succ 2013 \succ 2014 \succ 很好 \succ 良 \succ 很垃圾$
120.978028<$k$< 0.9789490.000921$2017 \succ 牛逼 \succ 2011 \succ 2015 \succ 2012 \succ 2013 \succ 2016 \succ 2014 \succ 很好 \succ 良 \succ 很垃圾$
130.978949<$k$< 0.9833260.004377$2017 \succ 牛逼 \succ 2011 \succ 2012 \succ 2015 \succ 2013 \succ 2016 \succ 2014 \succ 很好 \succ 良 \succ 很垃圾$
140.983326<$k$< 0.986270.002944$2017 \succ 牛逼 \succ 2011 \succ 2012 \succ 2015 \succ 2013 \succ 2014 \succ 2016 \succ 很好 \succ 良 \succ 很垃圾$
150.98627<$k$< 0.9916840.005414$2017 \succ 牛逼 \succ 2011 \succ 2012 \succ 2013 \succ 2015 \succ 2014 \succ 2016 \succ 很好 \succ 良 \succ 很垃圾$
160.991684<$k$< 10.008316$2017 \succ 牛逼 \succ 2011 \succ 2012 \succ 2013 \succ 2014 \succ 2015 \succ 2016 \succ 很好 \succ 良 \succ 很垃圾$

AECM运算之三,层级要素所占区段统计,统计矩阵的获得


层级,序号越小越优 要素所占区段,该层级要素的的占比
02017=1   
12016=0.450553   2015=0.299314   牛逼=0.250133   
22015=0.671405   2016=0.287486   牛逼=0.011828   2011=0.029281   
3牛逼=0.738039   2016=0.224767   2011=0.007913   2015=0.00823   2012=0.021051   
4很好=0.402835   2011=0.559971   2016=0.008057   2012=0.008086   2015=0.007321   2013=0.01373   
52011=0.402835   很好=0.008438   2012=0.55959   2016=0.007165   2013=0.008242   2015=0.005414   2014=0.008316   
62012=0.411273   很好=0.007575   2013=0.55918   2016=0.005298   2014=0.008358   2015=0.008316   
72013=0.418848   很好=0.005644   2014=0.558834   2016=0.016674   
82014=0.424492   很好=0.575508   
9良=1   
10很垃圾=1   

AECM运算之四,优胜与劣汰两种情境最终排序结果


情境 最优妥协解
优胜情境$2017 \succ 2016 \succ 2015 \succ 牛逼 \succ 2011 \succ 2012 \succ 2013 \succ 2014 \succ 很好 \succ 良 \succ 很垃圾$
劣汰情境 $2017 \succ 2016 \succ 2015 \succ 牛逼 \succ 2011 \succ 2012 \succ 2013 \succ 很好 \succ 2014 \succ 良 \succ 很垃圾$

扯蛋模型