摘要
水下航行器的噪声源识别具有训练样本有限,存在偶发或突变噪声源等特点。本文针对这些特点,在具有增量学习能力的水下航行器的噪声源识别系统架构下,提出了一种参数自适应可调的基于密度的聚类算法。实验表明,该算法可以有效避免基于密度的聚类算法的参数敏感性对聚类结果的不良影响,在无监督情况下对水下航行器的机械噪声源样本进行有效聚类。通过该聚类算法标注后的样本可直接作为具有增量学习结构的分类器的训练样本,节省了时间和系统开销。
The underwater vehicle machinery noise source recognition features that the training samples is limited and have abrupt noise samples. Based on these characteristics,this paper proposes a density-based algorithm which is parameter adjustable. And this novel algorithm is an important component of the underwater vehicle machinery noise source recognition system with incremental learning ability. The experimental results show the new algorithm can avoid the parameter sensitivity of DBSCAN. Labeled samples by this algorithm can directly be used as the classifier training samples,saving lots of time and system resources.
出处
《计算机工程与科学》
CSCD
北大核心
2010年第9期53-56,共4页
Computer Engineering & Science
基金
国家自然科学基金资助项目(50775218)
关键词
噪声源识别
增量学习
聚类算法
noise source recognition
incremental learning
clustering algorithms
