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Description: 同时辨识模型阶次及参数算法。用阶次递推算法，结合AIC法——利用赤池信息准则辨识上例的模型阶次和参数。-At the same time identify model order and parameters algorithm. By order recursive algorithm, combined with AIC method- the use of Akaike information criterion to identify the cases of the model order and parameters.
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Size: 55296 |
Author: 何林立 |
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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.
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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.
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Size: 2048 |
Author: 张镇 |
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Description: 将非平稳时间序列的状态空间建模方法用于陀螺过渡过程的分析.基于平滑先验约束的概念，使用卡尔曼滤波和赤池的AIC方法拟合全局模型，得到陀螺漂移模型的若干数值结果并用于陀螺系统分析.由于观测序列的趋势项、不规则分量可同时建模，因此比分别建模在统计上更加准确有效.-Will be non-stationary time series state space modeling method for the analysis of the transition process gyro. Priori smoothness constraint based on the concept, the use of Kalman filtering and Akaike s AIC method of fitting the overall model, the gyro drift model and a number of numerical results in the gyro system analysis. Due to the trend observed sequence, the irregular component may be at the same time modeling, modeling than were statistically more accurate and effective.
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Author: 我爱 |
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Description: 算例及matlab程序
一、 利用行列式比估计模型的阶次
二、 利用残差的方差估计模型的阶次三、 利用Akaike准则估计模型的阶次四、 利用最终预报误差准则估计模型的阶次
五、 根据Hankel矩阵的秩估计模型的阶次
附录1 利用行列式比估计模型的阶次
附录2 利用残差的方差估计模型的阶次
附录3 利用Akaike准则估计模型的阶次
附录4 利用最终预报误差准则估计模型的阶次
附录5 利用Hankel矩阵的秩估计模型的阶次-jie ci bian shi
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Size: 376832 |
Author: winwind |
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Description: Akaike阶次辨识在有色噪声的情况下通过计算AIC的值得到最终的结果。-Akaike order identification in the case of colored noise by calculating the AIC' s worth to the final result.
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Size: 1024 |
Author: 侯宁 |
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Description: Akaike阶次辨识在白噪声的情况下通过计算AIC的值得到最终的结果。-Akaike order identification in the case of white noise by calculating the AIC' s worth to the final result.
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Size: 1024 |
Author: 侯宁 |
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Description: RATS 信息准则，计量经济学中用于确定最优参数个数的关键准则-Akake information criterion
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Size: 1024 |
Author: wxwei08 |
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Description: 赤池准则 可以实现噪声检测 P波震相捡拾 S波震相捡拾等地震学基本操作等-Akaike criterion can achieve noise detection P-wave seismic phase picking S phases pick up basic operations such as seismology, etc.
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Size: 6144 |
Author: xiaji |
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Description: 为sinudoidal模型的AIC阶估计.AIC信息准则即Akaike information criterion，是衡量统计模型拟合优良性的一种标准，又由于它为日本统计学家赤池弘次创立和发展的，因此又称赤池信息量准则。它建立在熵的概念基础上，可以权衡所估计模型的复杂度和此模型拟合数据的优良性。
在一般的情况下，AIC可以表示为： AIC=2k-2ln(L)
其中：k是参数的数量，L是似然函数。 假设条件是模型的误差服从独立正态分布。 让n为观察数，RSS为剩余平方和，那么AIC变为： AIC=2k+nln(RSS/n)
增加自由参数的数目提高了拟合的优良性，AIC鼓励数据拟合的优良性但是尽量避免出现过度拟合(Overfitting)的情况。所以优先考虑的模型应是AIC值最小的那一个。赤池信息准则的方法是寻找可以最好地解释数据但包含最少自由参数的模型。-AIC order estimation for sinudoidal model
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Size: 1024 |
Author: Jay |
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Description: 同时辨识模型阶次及参数算法。用阶次递推算法，结合AIC法——利用赤池信息准则辨识上例的模型阶次和参数。-请键入文字或网站地址，或者上传文档。
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Tóngshí biànshì móxíng jiē cì jí cānshù suànfǎ. Yòng jiē cì dì tuīsuàn fǎ, jiéhé AIC fǎ——lìyòng chìchí xìnxī zhǔnzé biànshì shàng lì de móxíng jiē cì hé cānshù.At the same model order and parameters identification algorithm. By order recursive algorithm, combined with AIC method- use Akaike information criterion identification model order and parameters of the previous example.
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Size: 2115584 |
Author: 董毅 |
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Description: The Akaike information criterion (AIC) is a measure of the relative quality of a statistical model for a given set of data. That is, given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Hence, AIC provides a means for model selection.
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Size: 1024 |
Author: khalid |
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Description: AIC & MDL
The Akaike information criterion (AIC) is a measure of the relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Hence, AIC provides a means for model selection.
AIC is founded on information theory: it offers a relative estimate of the information lost when a given model is used to represent the process that generates the data. In doing so, it deals with the trade-off between the goodness of fit of the model and the complexity of the model.
AIC does not provide a test of a model in the sense of testing a null hypothesis i.e. AIC can tell nothing about the quality of the model in an absolute sense. If all the candidate models fit poorly, AIC will not give any warning of that.-AIC & MDL
The Akaike information criterion (AIC) is a measure of the relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Hence, AIC provides a means for model selection.
AIC is founded on information theory: it offers a relative estimate of the information lost when a given model is used to represent the process that generates the data. In doing so, it deals with the trade-off between the goodness of fit of the model and the complexity of the model.
AIC does not provide a test of a model in the sense of testing a null hypothesis i.e. AIC can tell nothing about the quality of the model in an absolute sense. If all the candidate models fit poorly, AIC will not give any warning of that.
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Size: 1024 |
Author: Said |
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Description: 逐步回归的基本思想是将变量逐个引入模型，每引入一个解释变量后都要进行F检验，并对已经选入的解释变量逐个进行t检验，当原来引入的解释变量由于后面解释变量的引入变得不再显著时，则将其删除。以确保每次引入新的变量之前回归方程中只包含显著性变量。这是一个反复的过程，直到既没有显著的解释变量选入回归方程，也没有不显著的解释变量从回归方程中剔除为止。以保证最后所得到的解释变量集是最优的。(In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure.[1][2][3][4] In each step, a variable is considered for addition to or subtraction from the set of explanatory variables based on some prespecified criterion. Usually, this takes the form of a sequence of F-tests or t-tests, but other techniques are possible, such as adjusted R2, Akaike information criterion, Bayesian information criterion, Mallows's Cp, PRESS, or false discovery rate.)
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Size: 2048 |
Author: 宫晓楠
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