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ELM

于 2016-11-21 发布 文件大小:7103KB
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下载积分: 1 下载次数: 116

代码说明:

  elm极限学习机预测风速 预测精度高能够形成完成的预测风速 (elm ELM forecast wind speed high prediction accuracy can be formed to complete the predicted wind speed)

文件列表:

ELM
...\elman1.m,571,2015-10-21
...\elmpredict.m,624,2015-05-20
...\elmtrain.m,864,2015-05-20
...\exnet.mat,7886668,2015-10-20
...\Qu.m,837,2015-05-20
...\Queue.m,1110,2015-05-20
...\Untitled.m,8122,2015-10-20
...\Untitled1.asv,571,2015-05-24
...\Untitled1.m,571,2015-05-24

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    用matlab实现卡农演奏。。。。。。。。。。(Canon played with matlab)
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    Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.
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