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文件名称:nnfpe
介绍说明－－下载内容来自于网络，使用问题请自行百度
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.
％ 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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下载文件列表
nnfpe.m
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