纺织学报 ›› 2019, Vol. 40 ›› Issue (05): 124-130.doi: 10.13475/j.fzxb.20180607907

• 服装工程 • 上一篇    下一篇

基于改进层次分析法的特殊体型样板识别

周捷1(), 李健1, 马秋瑞1, 黄晓杰2   

  1. 1.西安工程大学 服装与艺术设计学院, 陕西 西安 710048
    2.中原工学院 信息商务学院, 河南 郑州 450007
  • 收稿日期:2018-06-25 修回日期:2019-01-29 出版日期:2019-05-15 发布日期:2019-05-21
  • 作者简介:周捷(1969—),女,副教授,博士。主要研究方向为功能性内衣、服装结构和人体科学等。E-mail: xianzj99@163.com。
  • 基金资助:
    陕西省科技厅国际科技合作计划项目(2018KW-056);陕西高等教育教改研究项目(17BZ037)

Recognition of special template based on improved analytic hierarchy process

ZHOU Jie1(), LI Jian1, MA Qiurui1, HUANG Xiaojie2   

  1. 1. School of Apparel and Art Design, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
    2. College of Information & Business, Zhongyuan University of Technology, Zhengzhou, Henan 450007, China
  • Received:2018-06-25 Revised:2019-01-29 Online:2019-05-15 Published:2019-05-21

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摘要：

针对传统层次分析法的缺陷,提出结合因子分析、聚类分析的改进层次分析方法,应用到特体样板识别中。首先,通过因子分析确定各级指标的影响因素及其权重系数。然后,采用K-means聚类分析将特体样板聚为臀部、胸部和腹部等3类典型样板。最后,运用层次分析法确定各指标的权重,构建了包括6个二级指标、13个三级指标、3个四级指标的特体样板递阶层次结构模型。随机选取3个样本进行实证研究,以因子分析中提取的5个主成分为聚类指标,以K-means方法进行聚类,采用特体样板递阶层次结构模型识别被测样本与臀部、胸部、腹部样板的隶属度。结果表明,该方法可有效地表征特体样板的变异程度并识别所属的样板类别。

关键词: 特体样板, 因子分析, 聚类分析, 改进层次分析法

Abstract:

Aiming at the shortcomings of conventional analytic hierarchy process, an improved analytic hierarchy process based on combination factor analysis and cluster analysis was proposed and applied to special model recognition. Firstly, factor analysis was applied to determine the influencing factors and weight coefficients of each level of indicators. Then, K-means cluster analysis was applied to collect the special samples into three typical models such as hip, chest and abdomen. Finally, the analytic hierarchy process was applied to determine the weight of each indicator, and a special model-level hierarchical structure model including six secondary indicators, 13 third-level indicators and three four-level indicators was constructed. Three samples were randomly selected for empirical research. The five principal components extracted from the factor analysis were used as clustering indicators, and the K-means method was used for clustering. The specific sample-level hierarchical structure model was applied to identify the sample to be tested and hip and chest. The results show that the method can effectively characterize the variation of the special model and identify the model category.

Key words: special template, factor analysis, cluster analysis, improved analytic hierarchy process

中图分类号: 

  • TS941.712

图1

特体修正样板的典型指标"

图2

改进层次分析法流程图"

表1

主成分因子分析"

成分 初始特征值 因子旋转后
特征根 方差贡
献率/%		 累计贡
献率/%		 特征根 方差贡
献率/%		 累计贡
献率/%
1 3.232 24.865 24.865 2.740 21.076 21.076
2 2.281 17.545 42.410 2.311 17.777 38.853
3 1.882 14.473 56.884 2.189 16.840 55.693
4 1.139 8.764 65.648 1.274 9.800 65.493
5 1.027 7.902 73.550 1.047 8.057 73.550

表2

旋转载荷矩阵"

评价
指标		 成分
1 2 3 4 5
C1 -0.004 -0.117 0.898 0.019 -0.053
C2 0.826 -0.133 0.075 -0.010 0.124
C3 0.557 0.241 0.017 -0.267 0.116
C4 0.095 0.537 -0.296 -0.062 -0.531
C5 0.734 0.202 -0.003 0.489 -0.190
C6 0.793 0.083 -0.148 -0.132 0.011
C7 -0.035 -0.087 0.716 0.123 0.141
C8 0.068 0.952 -0.035 0.032 0.102
C9 0.742 0.045 -0.069 0.501 -0.194
C10 -0.031 0.090 0.854 -0.063 -0.114
C11 0.073 0.253 -0.106 0.055 0.765
C12 -0.061 0.007 0.081 0.817 0.126
C13 -0.075 -0.947 0.007 -0.024 -0.128

表3

方差分析表"

聚类数 变量 类间
均方		 误差
均方		 F值 Sig.
3 因子1 35.603 0.810 43.960 0.000
因子2 96.744 0.474 204.132 0.000
因子3 65.601 0.645 101.699 0.000
因子4 6.849 0.968 7.077 0.001
因子5 8.209 0.960 8.547 0.000
4 因子1 16.043 0.876 18.321 0.000
因子2 45.592 0.631 72.200 0.000
因子3 38.712 0.688 56.241 0.000
因子4 60.413 0.509 118.692 0.000
因子5 37.000 0.702 52.671 0.000
5 因子1 35.831 0.615 58.250 0.000
因子2 50.323 0.455 110.602 0.000
因子3 40.349 0.565 71.390 0.000
因子4 49.733 0.462 107.758 0.000
因子5 15.047 0.845 17.811 0.000

表4

3类特体样板主要部位平均修正量统计表"

样板类型 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13
D1 7.72 8.95 -2.32 -7.94 8.96 11.56 1.27 -2.43 7.41 -1.39 -1.85 0.17 2.00
D2 -0.85 -6.17 -8.75 -12.13 -9.95 -3.68 -2.42 -13.35 -1.95 -10.43 -9.37 -0.30 13.02
D3 -32.73 -5.28 -12.13 -2.09 3.55 4.60 -6.51 -0.78 4.68 -15.28 -2.28 0.61 0.76

图3

特体样板识别模型的递阶层次结构"

表5

判断矩阵标度aij含义"

标度(aij ) 含义
1 元素i与元素j同等重要
3 元素i比元素j稍重要
5 元素i比元素j颇重要
7 元素i比元素j极重要
9 元素i比元素j极端重要
2/4/6/8 分别介于以上标度的中间值
倒数 若元素j与元素i的关系是: aji=1/aij

表6

特体样板识别判断矩阵B-A"

类别 B1 B2 B3 B4 B5 权重
B1 1 7/6 21/17 7/3 21/8 0.290 0
B2 6/7 1 15/14 19/11 19/9 0.238 8
B3 17/21 14/15 1 19/11 2 0.227 2
B4 3/7 11/19 11/19 1 5/4 0.133 1
B5 8/21 9/19 1/2 4/5 1 0.110 9

表7

廓形因子判断矩阵C-B1"

类别1 C2 C3 C5 C6 C9 权重
C2 1 7 5 3 4 0.484 5
C3 1/7 1 1/3 1/5 1/4 0.043 4
C5 1/5 3 1 1/3 1/2 0.089 8
C6 1/3 5 3 1 3 0.249 4
C9 1/4 4 2 1/3 1 0.132 9

表8

待测样本与特体样板主要部位尺寸差值大小"

部位
指标		 修正量 样本a与3类特体样板差值 样本b与3类特体样板差值 样本c与3类特体样板差值
样本a 样本b 样本c Δa1 Δa2 Δa3 Δb1 Δb2 Δb3 Δc1 Δc2 Δc3
C1 30 70 -70 22.28 30.85 62.73 62.28 70.85 102.73 -77.72 -69.15 -37.27
C2 -30 60 -40 -38.95 -23.83 -24.72 51.05 66.17 65.28 -48.95 -33.83 -34.72
C3 -15 0 -15 -12.68 -6.25 -9.38 2.32 8.75 5.62 -12.68 -6.25 -9.38
C4 -12 -20 -15 -4.06 0.13 -9.91 -12.06 -7.87 -17.91 -7.06 -2.87 -12.91
C5 8 45 -60 -0.96 17.95 4.45 36.04 54.95 41.45 -68.96 -50.05 -63.55
C6 -15 10 -28 -26.56 -11.32 -19.6 -1.56 13.68 5.4 -39.56 -24.32 -32.6
C7 -10 15 -15 -11.27 -7.58 -3.49 13.73 17.42 21.51 -16.27 -12.58 -8.49
C8 0 0 -10 2.43 13.35 0.78 2.43 13.35 0.78 -7.57 3.35 -9.22
C9 3 45 -40 -4.41 4.95 -1.68 37.59 46.95 40.32 -47.41 -38.05 -44.68
C10 10 35 -35 11.39 20.43 25.82 36.39 45.43 50.82 -33.61 -24.57 -19.18
C11 0 5 0 1.85 9.37 2.28 6.85 14.37 7.28 1.85 9.37 2.28
C12 10 0 0 9.83 10.3 9.39 -0.17 0.3 -0.61 -0.17 0.3 -0.61
C13 0 0 10 -2 -13.02 -0.76 -2 -13.02 -0.76 8 -3.02 9.24

表9

指标层总权重计算和排序"

二级指标 三级指标 四级指标单
排序权重
WD		 总排序权重
W=WB×
WC×WD
指标 权重
WB		 指标 单排序
权重WC
B1 0.290 0 C2 0.484 5 [0.700 7,0.097 2,
0.202 1]		 W1=
0.293 7,
W2=
0.436 4,
W3=
0.269 9
C3 0.043 4 [0.637 0, 0.104 7,
0.258 3]
C5 0.089 8 [0.058 1, 0.735 2,
0.206 7]
C6 0.249 4 [0.637 0, 0.104 7,
0.258 3]
C9 0.132 9 [0.296 9, 0.617 5,
0.085 6]
B2 0.238 8 C8 0.750 0 [0.218 5, 0.714 7,
0.066 8]
C13 0.250 0 [0.148 8, 0.785 4,
0.065 8]
B3 0.227 2 C1 0.637 0 [0.081 0, 0.188 4,
0.730 6]
C7 0.104 7 [0.637 0, 0.258 3,
0.104 7]
C10 0.258 3 [0.071 9, 0.279 0,
0.649 1]
B4 0.133 1 C12 1.000 [0.280 8, 0.584 2,
0.135 0]
B5 0.110 9 C4 0.166 7 [0.227 1, 0.051 0,
0.721 9]
C11 0.833 3 [0.081 0, 0.730 6,
0.188 4]

图4

特体样板类别雷达图"

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