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Description: 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
Platform: |
Size: 217088 |
Author: 陈栋 |
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Description: This function calculates Akaike s final prediction error
％ estimate of the average generalization error.
％
％ [FPE,deff,varest,H] = fpe(NetDef,W1,W2,PHI,Y,trparms) produces the
％ final prediction error estimate (fpe), the effective number of
％ weights in the network if the network has been trained with
％ weight decay, an estimate of the noise variance, and the Gauss-Newton
％ Hessian.
％-This function calculates Akaike s final prediction error estimate of the average generalization error. [FPE, deff, varest, H] = fpe (NetDef, W1, W2, PHI, Y, trparms) produces the final prediction error estimate ( fpe), the effective number of weights in the network if the network has been trained with weight decay, an estimate of the noise variance, and the Gauss-Newton Hessian.
Platform: |
Size: 2048 |
Author: 张镇 |
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Description:
Platform: |
Size: 40960 |
Author: pupu |
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Description: 确定时间序列中的阶数的FPE或AIC准则的matlab代码。-Determine the order of time for the FPE or AIC criterion by matlab.
Platform: |
Size: 1024 |
Author: 天外天 |
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Description: doarima.m MATLAB script to do an ARIMA(p,d,q) analysis where d is set but P and q can be vectors of values you want to try. It produces AIC and FPE (final prediction error) values for comparing models. The AIC is different than the one computed by S+, but they both minimize on the same model for the arma22.dat simulated data.
Platform: |
Size: 2048 |
Author: timyscralem |
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Description: 用MATLAB编程求AR参数，并采用ＦＰＥ估计AR阶数-AR parameters uated using MATLAB programming, and the use of FPE estimated AR order
Platform: |
Size: 113664 |
Author: 王飞 |
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Description: LWP是一种Matlab / Octave工具箱实现局部加权多项式回归（也被称为局部回归/局部加权散点平滑/黄土/ LOWESS和核平滑）。使用此工具箱，您可以使用九个具有度量窗口宽度或最近邻窗口宽度的任意一个内核来拟合任意维度的数据的局部多项式。还提供了一个优化内核带宽的函数。优化可采用留一交叉验证，GCV，AICC、AIC，FPE，T，执行，或单独的验证数据。鲁棒拟合也可用。(LWP is a Matlab/Octave toolbox implementing Locally Weighted Polynomial regression (also known as Local Regression / Locally Weighted Scatterplot Smoothing / LOESS / LOWESS and Kernel Smoothing). With this toolbox you can fit local polynomials of any degree using one of the nine kernels with metric window widths or nearest neighbor window widths to data of any dimensionality. A function for optimization of the kernel bandwidth is also available. The optimization can be performed using Leave-One-Out Cross-Validation, GCV, AICC, AIC, FPE, T, S, or separate validation data. Robust fitting is available as well.)
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Size: 5120 |
Author: baidudu |
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