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BER-of-Nakagami-fading-channel-master

于 2021-01-27 发布

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说明：开始您的研究。然后，您可以使用正态分布为这些其他值生成数字(Rayleigh Rician Weibu)

文件列表：

BER-of-Nakagami-fading-channel-master, 0 , 2020-09-06
BER-of-Nakagami-fading-channel-master\IPAM_NAKAGAMI.jpg, 59221 , 2020-09-06
BER-of-Nakagami-fading-channel-master\IPAM_NAKAGAMI.m, 5256 , 2020-09-06
BER-of-Nakagami-fading-channel-master\MPSK_NAKAGAMI.jpg, 63525 , 2020-09-06
BER-of-Nakagami-fading-channel-master\MPSK_NAKAGAMI.m, 5313 , 2020-09-06
BER-of-Nakagami-fading-channel-master\MQAM_NAKAGAMI.jpg, 68752 , 2020-09-06
BER-of-Nakagami-fading-channel-master\MQAM_NAKAGAMI.m, 5294 , 2020-09-06
BER-of-Nakagami-fading-channel-master\README.md, 1308 , 2020-09-06

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2020-06-25 18:00:01下载

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2020-07-22 21:58:44下载

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2023-07-28 02:30:04下载

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协同区间偏最小二乘 siPLS算法是 N rgaard等对其提出的 iPLS方法的改进, 其基本算法步骤如下:(1)对原始谱图进行预处理;(2)在全谱范围内建立全局偏最小二乘模型, 即上节的模型;(3)在整个光谱区间采用 iPLS建立多个等窗口宽度的子区间, 假设为 n个;(4)在每个子区间上建立偏最小二乘法模型, 即可得到 n个局部模型;(5)以交叉验证时的均方根误差 RMSE 值为各模型的精度衡量标准, 比较全光谱模型和各局部模型的精度;(6)组合精度最高的局部子区间为新的回归区间;再在新的回归区间建立 siPLS模型(Cooperative interval partial least squares (siPLS) algorithm is proposed by N rgaard et al. The improvements of the iPLS method are as follows: (1) the original spectrogram is performed. Preprocessing; (2) establishing a global partial least squares model in the full spectrum, that is, the upper part of the model. Model; (3) use iPLS to establish multiple equal window widths in the whole spectral interval. The interval is assumed to be n; (4) a partial least squares model is established on each sub interval. N local models can be obtained; (5) the root mean square error of cross validation is RMSE The values are the accuracy criteria of each model, and the comparison between the full spectral model and the local models is made. (6) the local interval with the highest combined precision is a new regression interval; and then new. The establishment of siPLS model in the regression interval)

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