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于 2021-04-20 发布

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说明： BP神经网络基于美国西储大学轴承故障数据(BP neural network based on bearing failure data of Western Reserve University)

文件列表：

程序\10964800_march_imf2_prediction.m, 7422 , 2020-06-16
程序\BP.m, 92615 , 2020-06-18
程序\BP1.m, 5306 , 2020-06-18
程序\BP2.m, 92838 , 2020-08-18
程序\learn.m, 7422 , 2020-06-17
程序\TDE.m, 92087 , 2020-06-24
程序\误差曲线.tif, 772331 , 2020-08-10

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forKPMtools
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2011-01-26 11:05:03下载

积分：1
youhuawenti
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2010-05-23 22:25:29下载

积分：1
fecgm
独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)

2010-05-27 23:08:51下载

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