runs Kalman-Bucy filter over observations matrix Z for 1-step Prediction onto matrix X (X can = Z) with model order p V = initial covariance of observation sequence noise returns model parameter estimation sequence A, sequence of predicted outcomes y_pred and error matrix Ey (reshaped) for y and Ea for a along with inovation prob P = P(y_t | D_t-1) = evidence
標(biāo)簽： matrix observations Kalman-Bucy Prediction
上傳時間： 2016-04-28
上傳用戶：huannan88
support vector machine based Prediction
標(biāo)簽： Prediction support machine vector
上傳時間： 2013-12-03
上傳用戶：zhangzhenyu
股票價格預(yù)算Stock Prediction Based on Price Patterns （國外原程序包）
標(biāo)簽： Prediction Patterns Stock Based
上傳時間： 2014-01-21
上傳用戶：wyc199288
機器學(xué)習(xí)經(jīng)典書籍The Elements of Statistical Learning--Data Mining, Inference and Prediction. 作者：Friedman
標(biāo)簽： Statistical Prediction Inference Elements
上傳時間： 2014-12-03
上傳用戶：奇奇奔奔
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
analog device vdsp branch Prediction tutorial
標(biāo)簽： Prediction tutorial analog device
上傳時間： 2013-12-27
上傳用戶：JIUSHICHEN
Ansys code for the life Prediction of melt
標(biāo)簽： Prediction Ansys code life
上傳時間： 2013-12-31
上傳用戶：二驅(qū)蚊器
ANN Prediction using Excel
標(biāo)簽： Prediction Excel using ANN
上傳時間： 2017-04-03
上傳用戶：xuanjie
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