FPGA-based link layer chip S19202 configuration
標簽： configuration FPGA-based S19202 layer
上傳時間： 2013-12-27
上傳用戶：z1191176801
MultiBand OFDM Physical Layer Proposal
標簽： MultiBand Physical Proposal Layer
上傳時間： 2013-12-27
上傳用戶：天誠24
iMX21 SDHC driver(PDD layer)
上傳時間： 2013-12-19
上傳用戶：waitingfy
YAMAHA YMU762 IC MA-3 Sound Middleware sample source code , Include device driver , Operation API
標簽： Middleware Operation Include YAMAHA
上傳時間： 2014-01-08
上傳用戶：wanqunsheng
以太網物理層接口器件-10/100 Mbps Fast Ethernet Physical Layer TX/FX Single Chip Transceiver
標簽： Transceiver Ethernet Physical Single
上傳時間： 2016-10-13
上傳用戶：jackgao
this an example showing how the threading policy work ! It is the topic of the middleware. This program involves CORBA also.
標簽： the middleware threading example
上傳時間： 2013-11-30
上傳用戶：sqq
how to use the bluetooth module in the HCI layer
標簽： bluetooth the module layer
上傳時間： 2013-11-25
上傳用戶：yzy6007
文件操作系統（UcosII) File system s OS Layer file
上傳時間： 2016-12-12
上傳用戶：1109003457
％ 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
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
