A PAINLESS GUIDE TO CRC ERROR DETECTION ALGORITHMS CRC校驗理論與實踐的經(jīng)典教程，Ross寫的。
標(biāo)簽： ALGORITHMS CRC DETECTION PAINLESS
上傳時間： 2016-12-15
上傳用戶：gxf2016
This package provides encoders and fast Viterbi decoders for the NASA standard rate 1/2 and rate 1/3 constraint length 7 convolutional codes.
標(biāo)簽： rate and encoders decoders
上傳時間： 2013-12-18
上傳用戶：彭玖華
msp430心電儀程序代碼 ............\Heart rate ............\Heart rate with DAC output ............\Heart rate with EKG Demo ............\mul.s43（需要與上面三個之一配合使用）
標(biāo)簽： Heart rate output with
上傳時間： 2016-12-24
上傳用戶：曹云鵬
microsoft sqlserver internal error!
標(biāo)簽： microsoft sqlserver internal error
上傳時間： 2013-12-27
上傳用戶：lacsx
Generate trellis data of a rate-1/n convolutional encoder.卷積碼1/n的編碼器，注意生成的是非系統(tǒng)碼。
標(biāo)簽： convolutional Generate trellis encoder
上傳時間： 2014-12-20
上傳用戶：ghostparker
Generate trellis of a rate-1/n recursive convolutional code，生成網(wǎng)格圖（對碼率為1/n的遞歸卷積碼）
標(biāo)簽： convolutional recursive Generate trellis
上傳時間： 2013-12-09
上傳用戶：xz85592677
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. ％
標(biāo)簽： generalization calculates prediction function
上傳時間： 2014-12-03
上傳用戶：maizezhen
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. ％
標(biāo)簽： generalization calculates prediction function
上傳時間： 2016-12-27
上傳用戶：腳趾頭
Train a two layer neural network with a recursive prediction error ％ algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully ％ connected) networks can be trained. ％ ％ The activation functions can either be linear or tanh. The network ％ architecture is defined by the matrix NetDef , which has of two ％ rows. The first row specifies the hidden layer while the second ％ specifies the output layer.
標(biāo)簽： recursive prediction algorithm Gauss-Ne
上傳時間： 2016-12-27
上傳用戶：ljt101007
Error probability performance for W-CDMA systems with multiple transmit and receive antennas in correlated Nakagami fading channels
標(biāo)簽： probability performance antennas multiple
上傳時間： 2014-01-08
上傳用戶：WMC_geophy
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