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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
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