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Description: 最全的外挂制作教程
本教程包含了（WPE，FPE）编程类（动作式，单机游戏改式，盗号工具制作，加速式外挂，网络游戏数据修
改）
Platform: |
Size: 937111 |
Author: ch |
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Description: 生成一组带有高斯噪声的样本,分别用一阶,二阶,三阶的最小二乘估计方法进行拟合,然后分别用AIC,MDL,FPE,CAT四种评测模型对其性能进行比较,得到最优的拟合模型.
Platform: |
Size: 984 |
Author: 狐狸 |
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Description: 在系统辨识过程中,系统介数未知,估计模型阶次的FPE法
Platform: |
Size: 13807 |
Author: 徐徐 |
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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
Platform: |
Size: 13817 |
Author: 陈栋 |
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Description: IBE是一种非对称密码技术，本源代码为IBE服务端源代码。-IBE is a non-symmetrical cryptographic techniques, source code for the IBE Server source code.
Platform: |
Size: 49152 |
Author: zhu |
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Description: 生成一组带有高斯噪声的样本,分别用一阶,二阶,三阶的最小二乘估计方法进行拟合,然后分别用AIC,MDL,FPE,CAT四种评测模型对其性能进行比较,得到最优的拟合模型.
Platform: |
Size: 1024 |
Author: 狐狸 |
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Description: 在系统辨识过程中,系统介数未知,估计模型阶次的FPE法-In the system identification process, the system referred to the number of the unknown, the estimated model order of the FPE method
Platform: |
Size: 125952 |
Author: 徐徐 |
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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: This function calculates Akaike s final prediction error
％ estimate of the average generalization error for network
％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive
％ counterparts.
％
％ [FPE,deff,varest,H] = nnfpe(method,NetDef,W1,W2,U,Y,NN,trparms,skip,Chat)
％ produces the final prediction error estimate (fpe), the effective number
％ of weights in the network if it 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 for network models generated by NNARX, NNOE, NNARMAX1+ 2, or their recursive counterparts. [FPE, deff, varest, H] = nnfpe (method , NetDef, W1, W2, U, Y, NN, trparms, skip, Chat) produces the final prediction error estimate (fpe), the effective number of weights in the network if it 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: 本教程包含了（WPE，FPE）编程类（动作式，单机游戏改式，盗号工具制作，加速式外挂，网络游戏数据修改），还有丰富源码。--This includes a tutorial (WPE, FPE) programming type (action-style, stand-alone game to style, instrument produced ones to speed up the style plug-ins, online game data modifications), have a rich source.--
Platform: |
Size: 1201152 |
Author: 张靓 |
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Description: 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。
-Proceedings in accordance with the model first order recursive algorithm for parameter estimation model, and then approach fpe order to determine the model.
Platform: |
Size: 13312 |
Author: Sun Yadong |
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Description: 按FPE定阶的依阶次递推算法程序，采用C语言编写-Determined by FPE-order sub-bands in accordance with procedures for recursive algorithm, using C language
Platform: |
Size: 6144 |
Author: aimar |
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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: 类似FPE和GameMaster的游戏修改源码，可以参考借鉴下实现原理-FPE and the GameMaster game similar modified source code, can refer to the principle of reference to achieve
Platform: |
Size: 3508224 |
Author: david |
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Description: 数学统计分析基本函数，自回归分析，包括AIC，FPE等确定阶数。-Autogression
Platform: |
Size: 2048 |
Author: 曾思栋 |
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Description: AR模型的C++实现
用于计算AR模型的a和E。
能自设阶数或者用FPE方法求最合适的技术-AR model of the C++ implementation of a model used to calculate the AR and E. To own or use the FPE method of order seeking the most appropriate technology
Platform: |
Size: 586752 |
Author: liu |
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Description: 按FPE定阶的依阶次递推算法程序。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。-By order given by FPE order recursive algorithm. First with a recursive algorithm to estimate model parameters according to model order, then fpe way to determine the order of the model.
Platform: |
Size: 386048 |
Author: 董毅 |
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Description: 使用FPE准则，绘制不同阶数的FPE值，确定最优阶数(Using the FPE criterion, the FPE values of different orders are plotted, and the optimal order is determined)
Platform: |
Size: 1024 |
Author: 阿峰峰
|
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