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用于异质信息的信任区间交互式多属性识别方法

李双明 关欣 衣晓 吴斌

李双明, 关欣, 衣晓, 吴斌. 用于异质信息的信任区间交互式多属性识别方法[J]. 电子与信息学报, 2021, 43(5): 1282-1288. doi: 10.11999/JEIT200038
引用本文: 李双明, 关欣, 衣晓, 吴斌. 用于异质信息的信任区间交互式多属性识别方法[J]. 电子与信息学报, 2021, 43(5): 1282-1288. doi: 10.11999/JEIT200038
Shuangming LI, Xin GUAN, Xiao YI, Bin WU. A BI-TODIM Approach Used for Heterogeneous Information Fusion[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1282-1288. doi: 10.11999/JEIT200038
Citation: Shuangming LI, Xin GUAN, Xiao YI, Bin WU. A BI-TODIM Approach Used for Heterogeneous Information Fusion[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1282-1288. doi: 10.11999/JEIT200038

用于异质信息的信任区间交互式多属性识别方法

doi: 10.11999/JEIT200038
基金项目: 国防科技卓越青年科学基金(2017-JCJQ-ZQ-003),泰山学者工程专项经费(ts 201712072)
详细信息
    作者简介:

    李双明:男,1986年生,博士,主要研究方向为目标识别技术

    关欣:女,1978年生,博士,教授,主要研究方向为信息融合、电子对抗及智能计算

    衣晓:男,1975年生,博士,教授,主要研究方向为无线传感器网络,多源信息融合

    吴斌:男,1992年生,博士,主要研究方向为主要研究方向为无线传感器网络

    通讯作者:

    李双明 aminglishuang@126.com

  • 中图分类号: TN911.7; TN957

A BI-TODIM Approach Used for Heterogeneous Information Fusion

Funds: The National Defense Science and Technology Excellence Youth Talent Fund (2017-JCJQ-ZQ-003), The Taishan Scholar Engineering Special Fund (ts 201712072)
  • 摘要: 为了解决混合类型数据与专家知识等异质信息的融合决策问题,该文提出了基于信任区间的交互式多属性识别(BI-TODIM)方法。完善了混合类型数据的距离测度,根据信任区间的构建定理和灰关联方法构建了未知目标混合类型数据的信任区间,阐明了信任区间与直觉模糊数之间的等价关系,创建了混合类型数据和专家知识的识别决策模型,实现了特征层信息和决策层信息的统一表达;分析了基于信度函数的逼近理想解(BF-TOPSIS)方法的反转现象及算法的复杂度,定义了区间数的序关系,提出了BI-TODIM识别决策方法,及基于直觉模糊熵的未知权重计算方法。结合算例和目标识别案例,验证了该文方法在解决排序反转和异质信息融合方面的有效性,突出了该方法时间复杂度低、稳定性好、识别准确度高的优点。
  • 图  1  算法流程示意图

    图  2  直觉模糊信息的图形表示

    图  3  信任区间的图形表示

    图  4  未知目标1/2/3的前景值变化曲线

    表  1  本文方法及BF-TOPSIS1/2的计算结果

    排序方法备选方案集排序结果运行时间(s)
    本文方法{A1, A2, A3, A4}A1$ \succ $A2$ \succ $A4$ \succ $A30.003306
    BF-TOPSIS1A1$ \succ $A2$ \succ $A4$ \succ $A30.663752
    BF-TOPSIS2A1$ \succ $A2$ \succ $A4$ \succ $A30.789059
    BF-TOPSIS3A2$ \succ $A1$ \succ $A4$ \succ $A321.255864
    下载: 导出CSV

    表  2  分类结果比较(%)

    分类方法数据集
    IrisWineGlass
    KNN95.3372.4774.29
    LST-KSVC99.2794.2765.76
    FGGCA97.2297.1093.65
    WLTSVM98.0096.4049.91
    本文方案97.1096.2094.70
    下载: 导出CSV

    表  3  工作模式的数据取值范围

    类别特征参数
    RF(MHz)PRI(μs)PW(μs)Cr
    R1[4940, 5160][3680, 3750][0.6, 1.2][0.3800, 0.4041]
    R2[5420, 5520][3600, 3680][0.2, 0.5][0.6626, 0.6731]
    R3[5100, 5420][3580, 3650][1.6, 2.0][0.1622, 0.2294]
    R4[5160, 5220][3730, 3800][0.9, 1.4][0.6587, 0.6981]
    R5[5520, 5620][3450, 3550][1.2, 1.5][0.7776, 0.8098]
    下载: 导出CSV

    表  4  参数1下识别结果的正确率(%)

    目标本文方法BF-TOPSIS1方法
    本文权重权重1权重2本文权重权重1权重2
    197.391.860.687.286.033.6
    294.486.838.079.679.627.9
    397.192.352.488.787.833.2
    下载: 导出CSV
  • [1] YING Chengshuo, LI Yanlai, CHIN K, et al. A new product development concept selection approach based on cumulative prospect theory and hybrid-information MADM[J]. Computers & Industrial Engineering, 2018, 122: 251–261. doi: 10.1016/j.cie.2018.05.023
    [2] 陈可嘉, 陈萍. 基于三参数区间灰数的TOPSIS决策方法[J]. 系统工程与电子技术, 2019, 41(1): 124–130. doi: 10.3969/j.issn.1001-506X.2019.01.18

    CHEN Kejia and CHEN Ping. Decision making method of TOPSIS based on three-parameter interval grey numbers[J]. Systems Engineering and Electronics, 2019, 41(1): 124–130. doi: 10.3969/j.issn.1001-506X.2019.01.18
    [3] 关欣, 孙贵东, 衣晓, 等. 基于关联系数靶心距的混合多属性识别[J]. 航空学报, 2015, 36(7): 2431–2443. doi: 10.7527/S1000-6893.2014.0299

    GUAN Xin, SUN Guidong, YI Xiao, et al. Hybrid multiple attribute recognition based on coefficient of incidence bull’s-eye-distance[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(7): 2431–2443. doi: 10.7527/S1000-6893.2014.0299
    [4] LOURENZUTTI R and KROHLING R A. TODIM based method to process heterogeneous information[J]. Procedia Computer Science, 2015, 55: 318–327. doi: 10.1016/j.procs.2015.07.056
    [5] CHEN S, KUO Liwei, and ZOU Xinyao. Multiattribute decision making based on Shannon’s information entropy, non-linear programming methodology, and interval-valued intuitionistic fuzzy values[J]. Information Sciences, 2018, 465: 404–424. doi: 10.1016/j.ins.2018.06.047
    [6] CHENG Jin, FENG Yixiong, LIN Zhiqiang, et al. Anti-vibration optimization of the key components in a turbo-generator based on heterogeneous axiomatic design[J]. Journal of Cleaner Production, 2017, 141: 1467–1477. doi: 10.1016/j.jclepro.2016.09.217
    [7] 刁鹏飞, 王艳娇. 基于节点休眠的水下无线传感器网络覆盖保持分簇算法[J]. 电子与信息学报, 2018, 40(5): 1101–1107. doi: 10.11999/JEIT170787

    DIAO Pengfei and WANG Yanjiao. Coverage-preserving clustering algorithm for underwater sensor networks based on the sleeping mechanism[J]. Journal of Electronics &Information Technology, 2018, 40(5): 1101–1107. doi: 10.11999/JEIT170787
    [8] PAVLICIC D. Normalization affects the results of MADM methods[J]. Yugoslav Journal of Operations Research, 2001, 11(2): 251–265.
    [9] DEZERT J, HAN Deqiang, and YIN Hanlin. A new belief function based approach for multi-criteria decision-making support[C]. The 19th International Conference on Information Fusion, Heidelberg, Germany, 2016: 782–789.
    [10] 李双明, 关欣, 赵静, 等. 一种参数区间交叉类型的目标识别方法[J]. 北京航空航天大学学报, 2020, 46(7): 1307–1316.

    LI Shuangming, GUAN Xin, ZHAO Jing, et al. A methodology for target recognition with parameters of interval cross type[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7): 1307–1316.
    [11] IRPINO A and VERDE R. Dynamic clustering of interval data using a Wasserstein-based distance[J]. Pattern Recognition Letters, 2008, 29(11): 1648–1658. doi: 10.1016/j.patrec.2008.04.008
    [12] ACI M and AVCI M. K nearest neighbor reinforced expectation maximization method[J]. Expert Systems with Applications, 2011, 38(10): 12585–12591. doi: 10.1016/j.eswa.2011.04.046
    [13] NIE Qingfeng, JIN Lizuo, FEI Shumin, et al. Neural network for multi-class classification by boosting composite stumps[J]. Neurocomputing, 2015, 149: 949–956. doi: 10.1016/j.neucom.2014.07.039
    [14] SANCHEZ M A, CASTILLO O, CASTRO J R, et al. Fuzzy granular gravitational clustering algorithm for multivariate data[J]. Information Sciences, 2014, 279: 498–511. doi: 10.1016/j.ins.2014.04.005
    [15] SHAO Yuanhai, CHEN Weijie, WANG Zhen, et al. Weighted linear loss twin support vector machine for large-scale classification[J]. Knowledge-Based Systems, 2015, 73: 276–288. doi: 10.1016/j.knosys.2014.10.011
    [16] 黄颖坤, 金炜东, 葛鹏, 等. 基于多尺度信息熵的雷达辐射源信号识别[J]. 电子与信息学报, 2019, 41(5): 1084–1091. doi: 10.11999/JEIT180535

    HUANG Yingkun, JIN Weidong, GE Peng, et al. Radar emitter signal identification based on multi-scale information entropy[J]. Journal of Electronics &Information Technology, 2019, 41(5): 1084–1091. doi: 10.11999/JEIT180535
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出版历程
  • 收稿日期:  2020-01-09
  • 修回日期:  2020-10-23
  • 网络出版日期:  2020-12-07
  • 刊出日期:  2021-05-18

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