CIESC Journal ›› 2019, Vol. 70 ›› Issue (2): 581-589.DOI: 10.11949/j.issn.0438-1157.20180855
• Process system engineering • Previous Articles Next Articles
Jian ZHU(),Bo YANG,Yongjian WANG,Xiaojie TANG,Hongguang LI()
Received:
2018-07-25
Revised:
2018-11-30
Online:
2019-02-05
Published:
2019-02-05
Contact:
Hongguang LI
通讯作者:
李宏光
作者简介:
<named-content content-type="corresp-name">朱坚</named-content>(1992—),男,硕士研究生,<email>1915203154@qq.com</email>|李宏光(1963—),男,博士,教授,<email>lihg@mail.buct.edu.cn</email>
CLC Number:
Jian ZHU, Bo YANG, Yongjian WANG, Xiaojie TANG, Hongguang LI. New operation optimization method with time series based on Levenshtein distance hierarchical clustering[J]. CIESC Journal, 2019, 70(2): 581-589.
朱坚, 杨博, 王永健, 唐晓婕, 李宏光. 一种新型的基于Levenshtein距离层次聚类的时序操作优化方法[J]. 化工学报, 2019, 70(2): 581-589.
α | β 1 | β 2 | β 3 | β 4 | β 5 | β 6 | β 7 | β 8 | β 9 |
---|---|---|---|---|---|---|---|---|---|
3 | -0.43 | 0.43 | |||||||
4 | -0.67 | 0 | 0.67 | ||||||
5 | -0.84 | -0.25 | 0.25 | 0.84 | |||||
6 | -0.97 | -0.43 | 0 | 0.43 | 0.97 | ||||
7 | -1.07 | -0.57 | -0.18 | 0.18 | 0.57 | 1.07 | |||
8 | -1.05 | -0.67 | -0.32 | 0 | 0.32 | 0.67 | 1.15 | ||
9 | -1.22 | -0.76 | -0.43 | -0.14 | 0.14 | 0.43 | 0.76 | 1.22 | |
10 | -1.28 | -0.84 | -0.52 | -0.25 | 0 | 0.25 | 0.52 | 0.84 | 1.28 |
Table 1 Probability interval breakpoints
α | β 1 | β 2 | β 3 | β 4 | β 5 | β 6 | β 7 | β 8 | β 9 |
---|---|---|---|---|---|---|---|---|---|
3 | -0.43 | 0.43 | |||||||
4 | -0.67 | 0 | 0.67 | ||||||
5 | -0.84 | -0.25 | 0.25 | 0.84 | |||||
6 | -0.97 | -0.43 | 0 | 0.43 | 0.97 | ||||
7 | -1.07 | -0.57 | -0.18 | 0.18 | 0.57 | 1.07 | |||
8 | -1.05 | -0.67 | -0.32 | 0 | 0.32 | 0.67 | 1.15 | ||
9 | -1.22 | -0.76 | -0.43 | -0.14 | 0.14 | 0.43 | 0.76 | 1.22 | |
10 | -1.28 | -0.84 | -0.52 | -0.25 | 0 | 0.25 | 0.52 | 0.84 | 1.28 |
Algorithm | LD-hierarchical agglomerative |
---|---|
1 | inputs: 样本集合Ω={S 1,S 2,…,Sm },聚类簇编辑距离度量函数LD,聚类簇数目k=1. |
2 | for j = 1,2,…,m |
3 | Cj = {xj } |
4 | End for |
5 | for i = 1,2,…,m |
6 | for j = 1,2,…,m |
7 | M(i,j) = LD(Ci ,Cj ); |
8 | M(j,i) = M(i,j) |
9 | End for |
10 | End for |
11 | 设置当前簇数目q=m, |
12 | While q>k |
13 | 找出Levenshtein距离最近的两个簇Ci ,Cj ; |
14 | 合并Ci ,Cj ; |
15 | For j=j *+1,j *+2,…,q |
16 | 将簇Cj 重编号成Cj -1; |
17 | End for |
18 | 删除距离矩阵M第j *行和j *列; |
19 | For j=1,2,…,q-1 |
20 | M(i *, j) = LD( |
21 | M(j, i *) = M(i *, j); |
22 | End for |
23 | q = q -1 |
24 | End while |
25 | outputs: 簇划分C={C 1,C 2,…,Ck } |
Table 2 LD- hierarchical agglomerative clustering
Algorithm | LD-hierarchical agglomerative |
---|---|
1 | inputs: 样本集合Ω={S 1,S 2,…,Sm },聚类簇编辑距离度量函数LD,聚类簇数目k=1. |
2 | for j = 1,2,…,m |
3 | Cj = {xj } |
4 | End for |
5 | for i = 1,2,…,m |
6 | for j = 1,2,…,m |
7 | M(i,j) = LD(Ci ,Cj ); |
8 | M(j,i) = M(i,j) |
9 | End for |
10 | End for |
11 | 设置当前簇数目q=m, |
12 | While q>k |
13 | 找出Levenshtein距离最近的两个簇Ci ,Cj ; |
14 | 合并Ci ,Cj ; |
15 | For j=j *+1,j *+2,…,q |
16 | 将簇Cj 重编号成Cj -1; |
17 | End for |
18 | 删除距离矩阵M第j *行和j *列; |
19 | For j=1,2,…,q-1 |
20 | M(i *, j) = LD( |
21 | M(j, i *) = M(i *, j); |
22 | End for |
23 | q = q -1 |
24 | End while |
25 | outputs: 簇划分C={C 1,C 2,…,Ck } |
Symbol | Breakpoint | W/(kmol/h) |
---|---|---|
a b c d e | -3 | 104.4 |
-0.84 | 173.1 | |
-0.25 | 191.7 | |
0.25 | 207.6 | |
0.84 | 226.2 | |
3 | 294.9 |
Table 3 Raw data interval breakpoints
Symbol | Breakpoint | W/(kmol/h) |
---|---|---|
a b c d e | -3 | 104.4 |
-0.84 | 173.1 | |
-0.25 | 191.7 | |
0.25 | 207.6 | |
0.84 | 226.2 | |
3 | 294.9 |
x | ——液相摩尔分数 |
---|---|
W | ——蒸汽流量,kmol/h |
x | ——液相摩尔分数 |
---|---|
W | ——蒸汽流量,kmol/h |
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