MATLAB is a high-level language for technical computing which is often used by engineers to help them design systems or analyse a system’s behaviour.
標(biāo)簽： high-level computing engineers technical
上傳時(shí)間： 2014-01-11
上傳用戶：txfyddz
Newnes.Digital.Signal.Processing.System.Level.Design.Using.LabVIEW.Jun.2005.eBook-DDU labview信號(hào)處理教材。
標(biāo)簽： Processing eBook-DDU Digital LabVIEW
上傳時(shí)間： 2014-01-22
上傳用戶：gundan
臨床醫(yī)藥試驗(yàn)：Gehan s Two Stage Design
標(biāo)簽： Design Gehan Stage Two
上傳時(shí)間： 2013-12-10
上傳用戶：qw12
臨床醫(yī)藥試驗(yàn):Simon s Two stage Design
標(biāo)簽： Design Simon stage Two
上傳時(shí)間： 2014-01-10
上傳用戶：weiwolkt
Debugging with GDB, The GNU Source-Level Debugger Ninth Exlition,for GDB version6.6
標(biāo)簽： Source-Level Debugging GDB Debugger
上傳時(shí)間： 2016-12-16
上傳用戶：ynsnjs
*** *** *** *** *** *** ***** ** Two wire/I2C Bus READ/WRITE Sample Routines of Microchip s ** 24Cxx / 85Cxx serial CMOS EEPROM interfacing to a ** PIC16C54 8-bit CMOS single chip microcomputer ** Revsied Version 2.0 (4/2/92). ** ** Part use = PIC16C54-XT/JW ** Note: 1) All timings are based on a reference crystal frequency of 2MHz ** which is equivalent to an instruction cycle time of 2 usec. ** 2) Address and literal values are read in octal unless otherwise ** specified.
標(biāo)簽： Microchip Routines Sample WRITE
上傳時(shí)間： 2013-12-27
上傳用戶：ljmwh2000
Muscl Euler Two dimensions
標(biāo)簽： dimensions Muscl Euler Two
上傳時(shí)間： 2013-12-07
上傳用戶：363186
measure through the cross-entropy of test data. In addition, we introduce two novel smoothing techniques, one a variation of Jelinek-Mercer smoothing and one a very simple linear interpolation technique, both of which outperform existing methods.
標(biāo)簽： cross-entropy introduce smoothing addition
上傳時(shí)間： 2014-01-06
上傳用戶：qilin
％ 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(biāo)簽： Levenberg-Marquardt desired network neural
上傳時(shí)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(biāo)簽： recursive prediction algorithm Gauss-Ne
上傳時(shí)間： 2016-12-27
上傳用戶：ljt101007
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