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基于PCA和ICA模式融合的非高斯特征检测识别

葛泉波 程惠茹 张明川 郑瑞娟 朱军龙 吴庆涛

葛泉波, 程惠茹, 张明川, 郑瑞娟, 朱军龙, 吴庆涛. 基于PCA和ICA模式融合的非高斯特征检测识别. 自动化学报, 2024, 50(1): 169−180 doi: 10.16383/j.aas.c230326
引用本文: 葛泉波, 程惠茹, 张明川, 郑瑞娟, 朱军龙, 吴庆涛. 基于PCA和ICA模式融合的非高斯特征检测识别. 自动化学报, 2024, 50(1): 169−180 doi: 10.16383/j.aas.c230326
Ge Quan-Bo, Cheng Hui-Ru, Zhang Ming-Chuan, Zheng Rui-Juan, Zhu Jun-Long, Wu Qing-Tao. Non-Gaussian feature detection and recognition based on PCA and ICA pattern fusion. Acta Automatica Sinica, 2024, 50(1): 169−180 doi: 10.16383/j.aas.c230326
Citation: Ge Quan-Bo, Cheng Hui-Ru, Zhang Ming-Chuan, Zheng Rui-Juan, Zhu Jun-Long, Wu Qing-Tao. Non-Gaussian feature detection and recognition based on PCA and ICA pattern fusion. Acta Automatica Sinica, 2024, 50(1): 169−180 doi: 10.16383/j.aas.c230326

基于PCA和ICA模式融合的非高斯特征检测识别

doi: 10.16383/j.aas.c230326
基金项目: 国家自然科学基金(62033010, U23B2061), 中原科技创新领军人才(224200510004), 江苏高校青蓝工程(R2023Q07), 龙门实验室重大项目(231100220600), 河南省高校科技创新团队(24IRTSTHN022)资助
详细信息
    作者简介:

    葛泉波:南京信息工程大学教授. 主要研究方向为状态估计与信息融合, 自主智能无人系统, 飞行器测试数据分析和电力IOT技术. E-mail: 003535@nuist.edu.cn

    程惠茹:河南科技大学信息工程学院硕士研究生. 主要研究方向为状态估计与信息融合. E-mail: 210321050404@stu.haust.edu.cn

    张明川:河南科技大学信息工程学院教授. 主要研究方向为新型网络, 智能信息处理, 医疗辅助诊断和机器学习. 本文通信作者. E-mail: zhang_mch@haust.edu.cn

    郑瑞娟:河南科技大学信息工程学院教授. 主要研究方向为移动云计算, 生物启发的网络安全, 物联网安全和智能电网. E-mail: zhengruijuan@haust.edu.cn

    朱军龙:河南科技大学信息工程学院副教授. 主要研究方向为大规模优化, 分布式多智能体优化, 随机优化及其在机器学习中的应用. E-mail: jlzhu@haust.edu.cn

    吴庆涛:河南科技大学信息工程学院教授. 主要研究方向为工业互联网, 智能系统, 模式识别和机器学习. E-mail: wqt8921@hauet.edu.cn

Non-Gaussian Feature Detection and Recognition Based on PCA and ICA Pattern Fusion

Funds: Supported by National Natural Science Foundation of China (62033010, U23B2061), Leading Talents of Science and Technology in the Central Plain of China (224200510004), Qing Lan Project of Jiangsu Province (R2023Q07), Major Project of Longmen Laboratory (231100220600), and Scientific and Technological Innovation Team of Colleges and Universities in Henan Province (24IRTSTHN022)
More Information
    Author Bio:

    GE Quan-Bo Professor at Nanjing University of Information Science & Technology. His research interest covers state estimation and information fusion, autonomous intelligent unmanned system, aircraft test data analysis, and power IOT technology

    CHENG Hui-Ru Master student at the School of Information Engineering, Henan University of Science and Technology. Her research interest covers state estimation and information fusion

    ZHANG Ming-Chuan Professor at the School of Information Engineering, Henan University of Science and Technology. His research interest covers new generation network, intelligent information processing, medical aided diagnosis, and machine learning. Corresponding author of this paper

    ZHENG Rui-Juan Professor at the School of Information Engineering, Henan University of Science and Technology. Her research interest covers mobile cloud computing, biologically network security, internet of things security, and smart grid

    ZHU Jun-Long Associate professor at the School of Information Engineering, Henan University of Science and Technology. His research interest covers large-scale optimization, distributed multi-agent optimization, stochastic optimization, and their applications to machine learning

    WU Qing-Tao Professor at the School of Information Engineering, Henan University of Science and Technology. His research interest covers industrial internet, intelligent system, pattern recognition, and machine learning

  • 摘要: 针对无人船(Unmanned surface vehicle, USV)航行位姿观测数据的非高斯性/高斯性判别问题, 提出一种基于主成分分析(Principal component analysis, PCA)和独立成分分析(Independent component analysis, ICA) 模式融合的非高斯特征检测识别方法. 首先, 采用基于标准化加权平均和信息熵的数据预处理方法. 其次, 引入混合加权核函数并使用灰狼优化(Grey wolf optimization, GWO)算法进行参数优化, 以提高PCA方法的准确性. 同时, 该算法采用一种新的非线性控制因子策略, 提高全局和局部搜索能力. 最后, 建立了一种基于ICA和PCA联合的相关性分析方法来实现多维数据的降维, 在降维数据的基础上综合T型多维偏度峰度检验法和KS (Kolmogorov-Smirnov)检验法进行非高斯性/高斯性特征检测识别. 该方法考虑了非线性非高斯的噪声对降维结果精确度的影响, 有效降低了多维数据非高斯检测的复杂度, 同时也为后续在实际USV位姿估计等应用中提供了保障. 实验表明, 该方法具有较高的准确性和稳定性, 可为 USV 航行位姿观测数据处理提供支持.
  • 图  1  主成分分析改进方案过程

    Fig.  1  Principal component analysis improvement plan process

    图  2  变量$ {A} $与算法搜索的关系

    Fig.  2  The relationship between variable ${A} $ and algorithm search

    图  3  不同控制因子策略的迭代结果

    Fig.  3  Iterative results of different control factor strategies

    图  4  GWO参数优化流程图

    Fig.  4  GWO parameter optimization flowchart

    图  5  ICA-PCA融合过程图

    Fig.  5  ICA-PCA fusion process diagram

    图  6  实际数据采集环境

    Fig.  6  Actual data collection environment

    图  7  单峰函数结果对比图

    Fig.  7  Comparison chart of unimodal function results

    图  8  多峰函数结果对比图

    Fig.  8  Comparison chart of multimodal function results

    图  9  固定维数多峰函数结果对比图

    Fig.  9  Comparison chart of fixed dimension multimodal function results

    图  10  降维主成分结果

    Fig.  10  Dimensionality reduction principal component results

    图  11  三个方向的速度图

    Fig.  11  Chart of speed in three directions

    图  12  降维结果

    Fig.  12  Dimensionality reduction results

    图  13  检测结果1

    Fig.  13  Test result 1

    表  1  降维结果对比表

    Table  1  Comparison table of dimensionality reduction results

    方法名称累计方差贡献率 (%)
    8085909599
    原PCA方法576970110155
    EW-PCA方法67114578
    本文改进的PCA方法5693659
    下载: 导出CSV

    表  2  ICA-PCA方法对比结果

    Table  2  ICA-PCA method comparison results

    评价指标ICA-PCA方法本文改进方法
    主成分个数5348
    累计贡献率95%95%
    运行时间(s)64
    下载: 导出CSV

    表  3  降维结果

    Table  3  Dimensionality reduction results

    序数特征值方差百分比 (%)累计贡献率 (%)
    1542214.88938.96538.965
    2401455.50828.84967.814
    369059.8594.96372.777
    449084.3603.52776.304
    529370.8552.11178.414
    $ \vdots$$ \vdots$$ \vdots$$ \vdots$
    148118.4450.58387.186
    157161.2890.51587.701
    下载: 导出CSV

    表  4  正态性检验结果

    Table  4  Normality test results

    名称样本量平均值标准差偏度峰度Kolmogorov-Smirnov检验Shapiro-Wilk检验
    统计量DP统计量WP
    $x_{1}$10034.8252.5311.5281.2280.254**0.712**
    $x_{2}$10042.1638.8810.9361.0630.139**0.902**
    $x_{3}$10070.1867.3370.493−1.0390.166**0.881**
    $x_{4}$100311.51179.2140.138−1.1730.0830.0860.954**
    $ \vdots$$ \vdots$$ \vdots$$ \vdots$$ \vdots$$ \vdots$$ \vdots$$ \vdots$$ \vdots$$ \vdots$
    $x_{148}$10091.0162.455−0.092−0.8070.157**0.924**
    $x_{149}$1003.8013.2065.68840.0940.473**0.321**
    $x_{150}$10025.8928.4401.0150.2900.181**0.852**
    * 表示P < 0.05, ** 表示P < 0.01
    下载: 导出CSV

    表  5  非高斯检测结果

    Table  5  Non-Gaussian detection results

    检验结果
    总计N11643
    最大极差绝对0.049
    0.049
    −0.020
    检验统计量0.049
    渐进显著性(双边检验)0
    下载: 导出CSV
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  • 收稿日期:  2023-06-02
  • 录用日期:  2023-10-27
  • 网络出版日期:  2023-12-18
  • 刊出日期:  2024-01-29

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