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

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

葛泉波^{1, 2, 3,},
程惠茹^4,,
张明川^4,,
郑瑞娟^4,,
朱军龙^4,,
吴庆涛^4,

1.
南京信息工程大学自动化学院南京 210044
2.
江苏省大气环境与装备技术协同创新中心南京 210044
3.
江苏省大数据分析技术重点实验室南京 210044
4.
河南科技大学信息工程学院洛阳 471023

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

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出版历程
- 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

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

1.
School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044
2.
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing 210044
3.
Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing 210044
4.
School of Information Engineering, Henan University of Science and Technology, Luoyang 471023

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

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.
- 主成分分析 /
- 混合核函数 /
- 灰狼优化算法 /
- 高维降维 /
- 非高斯
Abstract: A non-Gaussian feature detection and recognition method based on principal component analysis (PCA) and independent component analysis (ICA) pattern fusion is proposed for the non-Gaussian/Gaussian discrimination problem of unmanned surface vehicle (USV) navigation pose observation data. Firstly, a data preprocessing approach based on standardization weighted average and information entropy is adopted. Secondly, a mixed weighted kernel function is introduced and the grey wolf optimization (GWO) algorithm is used for parameter optimization to enhance the accuracy of the PCA method. Moreover, a new non-linear control factor strategy is applied in the algorithm to improve both global and local search abilities. Finally, a correlation analysis method based on ICA and PCA joint is established to realize the dimensionality reduction of multidimensional data, and the non-Gaussian/Gaussian feature detection and recognition is carried out based on the comprehensive T-type multidimensional skewness kurtosis test and KS (Kolmogorov-Smirnov) test method on the basis of dimensionality reduction data. The proposed method takes into account the influence of nonlinear non-Gaussian noise on the accuracy of dimensionality reduction results, which can effectively reduce the complexity of non-Gaussian detection of multidimensional data, and also provide guarantee for the subsequent applications such as actual USV attitude estimation. Experimental results show high accuracy and stability, supporting the processing of USV navigation attitude observation data.
- Principal component analysis (PCA) /
- mixed kernel function /
- grey wolf optimization (GWO) algorithm /
- high-dimensional dimensionality reduction /
- non-Gaussian

HTML全文

图 1 主成分分析改进方案过程

Fig. 1 Principal component analysis improvement plan process

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图 2 变量$ {A} $与算法搜索的关系

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

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图 3 不同控制因子策略的迭代结果

Fig. 3 Iterative results of different control factor strategies

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图 4 GWO参数优化流程图

Fig. 4 GWO parameter optimization flowchart

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图 5 ICA-PCA融合过程图

Fig. 5 ICA-PCA fusion process diagram

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图 6 实际数据采集环境

Fig. 6 Actual data collection environment

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图 7 单峰函数结果对比图

Fig. 7 Comparison chart of unimodal function results

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图 8 多峰函数结果对比图

Fig. 8 Comparison chart of multimodal function results

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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

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图 10 降维主成分结果

Fig. 10 Dimensionality reduction principal component results

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图 11 三个方向的速度图

Fig. 11 Chart of speed in three directions

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图 12 降维结果

Fig. 12 Dimensionality reduction results

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图 13 检测结果1

Fig. 13 Test result 1

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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 (%)
方法名称	80	85	90	95	99
原PCA方法	57	69	70	110	155
EW-PCA方法	6	7	11	45	78
本文改进的PCA方法	5	6	9	36	59

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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	53	48
累计贡献率	95%	95%
运行时间(s)	6	4

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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 (%)
1	542214.889	38.965	38.965
2	401455.508	28.849	67.814
3	69059.859	4.963	72.777
4	49084.360	3.527	76.304
5	29370.855	2.111	78.414
$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$
14	8118.445	0.583	87.186
15	7161.289	0.515	87.701

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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
名称	样本量	平均值	标准差	偏度	峰度	统计量D值	P	统计量W值	P
$x_{1}$	100	34.82	52.531	1.528	1.228	0.254	**	0.712	**
$x_{2}$	100	42.16	38.881	0.936	1.063	0.139	**	0.902	**
$x_{3}$	100	70.18	67.337	0.493	−1.039	0.166	**	0.881	**
$x_{4}$	100	311.51	179.214	0.138	−1.173	0.083	0.086	0.954	**
$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$
$x_{148}$	100	91.01	62.455	−0.092	−0.807	0.157	**	0.924	**
$x_{149}$	100	3.80	13.206	5.688	40.094	0.473	**	0.321	**
$x_{150}$	100	25.89	28.440	1.015	0.290	0.181	**	0.852	**
* 表示P < 0.05, ** 表示P < 0.01

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表 5 非高斯检测结果

Table 5 Non-Gaussian detection results

检验结果
总计N		11643
最大极差	绝对	0.049
	正	0.049
	负	−0.020
检验统计量		0.049
渐进显著性(双边检验)		0

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