试用聚类分析对船只噪声和脑电分类
A trial of using cluster analysis for classifying ship noise and electroencephalogram
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摘要: 聚类分析属模式识别的一种方法,不同样本由于其信息特征有差异,经信号处理与计算机学习,可以在减维的特征空间被分类识别。本文介绍用非线性映射减维变换的方法,对船只噪声和脑电进行分类识别,想通过对两种不同领域里的样本试分类,来说明聚类分析方法的广泛适用性。Abstract: In multi-dimensional characteristic space a sample can be represented by its characteristic values. After nonlinear mapping proposed by Sammon, J. W. in 1969, in lower dimensional space of characteristics, samples with different characteristic values will be easily classified. On purpose to prove that closter analysis is suitable for quite different kinds of samples, in this paper some ship noises and some E E G samples are classified and shown. And it is worthy to point out that the adaptive step size expression of adaptive iteration deduced here could also be effective if it was applied to speed up convergence of adaptive algorithm used for signal processing.