Adaptive noise reduction by using cascaded sandglass‐type neural networks
Adaptive noise reduction by using cascaded sandglass‐type neural networks
复制标题
使用级联沙漏型神经网络进行自适应降噪
DOI：
10.1002/eej.1048
复制
发表时间：
2001
影响因子：
0.4
通讯作者：
K. Sugata
中科院分区：
文献类型：
--
作者：
Hiroki Yoshimura;Tadaaki Shimizu;N. Isu;K. Sugata
提出了一种由级联沙漏型神经网络（CSNNRF）构成的自适应降噪滤波器。给定数量的单元沙漏型神经网络（SNN），每个SNN都有三层结构，由输入层和输出层中相同数量的神经单元和隐藏层中的单个神经单元组成，级联连接。单位SNN的数量被自适应地确定为等于原始无噪声信号（信号分量）的协方差矩阵的秩。隐层单元的输出在个别单位SNN，其方差相当于观察到的信号的协方差矩阵的特征值，通过使用方差分析（ANOVA）进行统计比较，以估计排名。当CSNNRF由等于秩的单位SNN的数量组成时，在最大程度地减少噪声分量的同时，在没有任何信号分量损失的情况下对观测信号进行滤波。计算机实验表明，秩几乎总是以自适应的方式准确地估计，并且从信号中最佳地进行降噪。© 2001 Scripta Technica，Electr Eng Jpn，136（1）：37-46，2001
An adaptive noise reduction filter composed of cascaded sandglass‐type neural networks (CSNNRF) is proposed. A given number of unit sandglass‐type neural networks (SNN), each of which has a three‐layer structure and consists of the same number of neural units in the input and the output layers and a single neural unit the hidden layer, are connected in cascade. The number of unit SNNs is adaptively determined so as to be equal to a rank of covariance matrix of an original noise‐free signal (signal component). Outputs of hidden layer units in individual unit SNNs, whose variances are equivalent to eigenvalues of the covariance matrix of the observed signal, are statistically compared by use of ANOVA (analysis of variance) to estimate the rank. When a CSNNRF is composed of the number of unit SNNs equal to the rank, the observed signal is filtered without any loss of signal component while noise component is maximally reduced. It was shown by computer experiments that the rank was almost always estimated accurately in an adaptive manner, and that noise reduction from the signal was carried out optimally. © 2001 Scripta Technica, Electr Eng Jpn, 136(1): 37–46, 2001