neural NETwork 一個演示原理的代碼，便于初學者學習。
上傳時間： 2013-12-25
上傳用戶：181992417
k-step ahead predictions determined by simulation of the ％ one-step ahead neural NETwork predictor. For NNARMAX ％ models the residuals are set to zero when calculating the ％ predictions. The predictions are compared to the observed output. ％
標簽： ahead predictions determined simulation
上傳時間： 2016-12-27
上傳用戶：busterman
Produces a matrix of derivatives of NETwork output w.r.t. ％ each NETwork weight for use in the functions NNPRUNE and NNFPE.
標簽： NETwork w.r.t. derivatives Produces
上傳時間： 2013-12-18
上傳用戶：sunjet
％ Train a two layer neural NETwork with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is possible to use regularization by ％ weight decay. Also pruned (ie. not fully connected) NETworks can ％ be trained. ％ ％ Given a set of corresponding input-output pairs and an initial ％ NETwork, ％ [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms) ％ trains the NETwork with the Levenberg-Marquardt method. ％ ％ The activation functions can be either linear or tanh. The ％ NETwork architecture is defined by the matrix NetDef which ％ has two rows. The first row specifies the hidden layer and the ％ second row specifies the output layer.
標簽： Levenberg-Marquardt desired NETwork neural
上傳時間： 2016-12-27
上傳用戶：jcljkh
This function applies the Optimal Brain Surgeon (OBS) strategy for ％ pruning neural NETwork models of dynamic systems. That is NETworks ％ trained by NNARX, NNOE, NNARMAX1, NNARMAX2, or their recursive ％ counterparts.
標簽： function strategy Optimal Surgeon
上傳時間： 2013-12-19
上傳用戶：ma1301115706
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.
標簽： recursive prediction algorithm Gauss-Ne
上傳時間： 2016-12-27
上傳用戶：ljt101007
8139 NETwork card linux driver
上傳時間： 2016-12-28
上傳用戶：skhlm
structure EM算法 bayesian NETwork structure learning
標簽： structure bayesian learning NETwork
上傳時間： 2013-11-27
上傳用戶：ynsnjs
wireless sensor NETwork 電磁感應部分電路設計參考
標簽： wireless NETwork sensor 電磁感應
上傳時間： 2013-12-26
上傳用戶：sk5201314
wireless seneor NETwork 超聲傳感器部分電路原理圖設計
標簽： wireless NETwork seneor 超聲傳感器
上傳時間： 2014-01-01
上傳用戶：asdkin
