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FPE
说明: 按FPE定阶的
源程序:fpe.cpp
M序列:M序列.txt
白噪声:Gauss.txt
程序中先用依模型阶次递推算法估计模型的参数,再用fpe方法判断模型的阶次。
程序运行结果如下:
n: 1
判断阶次FPE的值: 0.0096406
-0.481665 1.07868
n: 2
判断阶次FPE的值: 0.00875755
-0.446739 0.00498181 1.07791 0.0527289
n: 3
判断阶次FPE的值: 0.0087098
-0.459433 0.120972 -0.0569228 1.07814 0.0390757 0.116982
n: 4
判断阶次FPE的值: 0.000396884
-0.509677 0.4501 -0.200906 0.0656188 1.07991 -0.0156362 0.442989 0.0497236
n: 5
判断阶次FPE的值: 3.2095e-007
-1.18415 0.813123 -0.517862 0.34881 -0.116864 1.07999 -0.744141 0.474462 -0.253112 0.122771
n: 6
判断阶次FPE的值: 3.23349e-007
-1.14659 0.76933 -0.487651 0.329676 -0.10377 -0.00440907 1.07999 -0.703574 0.447253 -0.235282 0.113587 0.00479688
从以上结果可以看出,当n=5时,fpe值最小,所以这时的模型阶次和参数估计值为最优结果:
3.2095e-007
-1.18415 0.813123 -0.517862 0.34881 -0.116864 1.07999 -0.744141 0.474462 -0.253112 0.122771(err)
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- 积分:1
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autofam
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7
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说明: 《M-files for "Neural Networks"》神经网络的一些MATLAB源程序,M-files for demos, exercises, and implementations.
作者:Herve Abdi ("M-files for" Neural Networks "," some of the neural network MATLAB source, M-files for demos, exercises, and implementations. Author : Herve Abdi)
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IHT
iterative hard thresholding algorithm in Compressed Sensing
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Acquisition-of-the-speech-signal
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