文章摘要
严良涛,项晓丽.基于核的k-最近邻在水下目标识别中的应用*[J].,2019,38(3):448-451
基于核的k-最近邻在水下目标识别中的应用*
Application of k-NN based on Kernel in underwater target recognition
投稿时间:2018-10-14  修订日期:2019-01-23
中文摘要:
      针对水中目标特征类型多、非线性强的特点,本文将K-KNN应用于水中目标识别。该方法采用PCA对特征矩阵进行降维,利用Kernel技巧将降维后的特征映射到高维空间进行KNN分类识别,并讨论了邻近点个数K对试验结果的影响。实际试验数据验证结果表明:与传统的KNN和BP神经网络分类器相比,K-KNN分类器的综合性能更优。
英文摘要:
      The targets underwater have many features and strong nonlinearity, this paper applies K-KNN to underwater target recognition.This method uses PCA to reduce the dimension of the feature matrix. Then the Kernel technique is used to map the reduced dimension to the high-dimensional space for KNN classification and recognition. The influence of the number K of adjacent points on the test results is discussed.The result of actual experimental data show that the K-KNN classifier has better overall performance than the traditional KNN and BP neural network classifiers.
DOI:10.11684/j.issn.1000-310X.2019.03.023
中文关键词: 水中目标识别  K-KNN  PCA  核函数
英文关键词: Underwater  target recognition,K-KNN,PCA,Kernel
基金项目:国家自然科学基金项目 (11774374)
作者单位E-mail
严良涛 中国人民解放军91388部队 xxlylt@sina.cn 
项晓丽 广州杰赛科技股份有限公司 xxlylt@sina.cn 
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