neural Network 一个演示原理的代码，便于初学者学习。
上传时间： 2013-12-25
上传用户：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. ％
标签： ahead predictions determined simulation
上传时间： 2016-12-27
上传用户：busterman
Produces a matrix of derivatives of Network output w.r.t. ％ each Network weight for use in the functions NNPRUNE and NNFPE.
标签： Network w.r.t. derivatives Produces
上传时间： 2013-12-18
上传用户：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.
标签： Levenberg-Marquardt desired Network neural
上传时间： 2016-12-27
上传用户：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.
标签： function strategy Optimal Surgeon
上传时间： 2013-12-19
上传用户：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.
标签： recursive prediction algorithm Gauss-Ne
上传时间： 2016-12-27
上传用户：ljt101007
8139 Network card linux driver
上传时间： 2016-12-28
上传用户：skhlm
structure EM算法 bayesian Network structure learning
标签： structure bayesian learning Network
上传时间： 2013-11-27
上传用户：ynsnjs
wireless sensor Network 电磁感应部分电路设计参考
标签： wireless Network sensor 电磁感应
上传时间： 2013-12-26
上传用户：sk5201314
wireless seneor Network 超声传感器部分电路原理图设计
标签： wireless Network seneor 超声传感器
上传时间： 2014-01-01
上传用户：asdkin
