the load forecast based on Neural Networks,use the MATLAB
標簽： the forecast Networks Neural
上傳時間： 2016-12-21
上傳用戶：iswlkje
A vision based navigation system for autonomous aircraft.pdf
標簽： autonomous navigation aircraft vision
上傳時間： 2016-12-22
上傳用戶：jackgao
實時障礙物識別 Realtime Obstacle Detection and Tracking Based on Constrained Delaunay Triangulation
標簽： Triangulation Constrained Detection Realtime
上傳時間： 2016-12-23
上傳用戶：shus521
Based on the frequency of single-chip design, can measure the number of low-frequency signal
標簽： low-frequency single-chip frequency the
上傳時間： 2016-12-25
上傳用戶：彭玖華
用JSP實現港口集裝箱管理系統。此系統基于BS結構的。Container Port Management Information System Based on BS
標簽： Information Management Container System
上傳時間： 2013-12-03
上傳用戶：royzhangsz
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
