| 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) |
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