This module provides an interface to an alphanumeric display module. The current version of this driver supports any alphanumeric LCD module based on the:Hitachi HD44780 DOT MATRIX LCD controller.
標簽： module alphanumeric interface provides
上傳時間： 2013-12-04
上傳用戶：himbly
書系統地介紹MATLAB 7.0的混合編程方法和技巧。全書共分為13章。第1章和第2章介紹MATLAB的基礎知識，第3章簡要介紹MATLAB混合編程，第4章至第9章分別介紹幾種典型的混合編程方法，包括C-MEX、MATLAB引擎、MAT數據文件共享、Mideva、Matrix和Add-in。第10章、第11章介紹MATLAB與Delphi和Excel的混合編程。第12章介紹MATLAB COM Builder，第13章以圖像處理為例介紹了一個綜合應用實例。 本書按混合編程的具體方法進行邏輯編排，自始至終用實例描述，每章著重闡述各種混合編程方法的實質和要點，同時穿插了作者多年使用MATLAB的經驗和體會。本書既適合初學者自學，也適用于高級MATLAB用戶，可作為高等數學、計算機、電子工程、數值分析、信息工程等課程的教學參考書，也可供上述領域的科研工作者參考。 本書所附光盤內容詳盡、實例豐富，包含MATLAB實例的源文件、函數/命令和注解以及程序實例。
上傳時間： 2013-12-24
上傳用戶：一諾88
the text file QMLE contains the quasi maximum likelyhood estimating procedure and performing Information Matrix test for a univariate GARCH(1,1) model
標簽： estimating likelyhood performing the
上傳時間： 2014-11-22
上傳用戶：zhenyushaw
常見java數據結構的使用方法，包括Arrays類Collections類HashSet類List類TreeSet類Map類Vector類
上傳時間： 2014-02-10
上傳用戶：qiao8960
These instances, whenmapped to an N-dimensional space, represent a core set that can be used to construct an approximation to theminimumenclosing ball. Solving the SVMlearning problem on these core sets can produce a good approximation solution in very fast speed. For example, the core-vector machine [81] thus produced can learn an SVM for millions of data in seconds.
標簽： N-dimensional whenmapped instances represent
上傳時間： 2016-11-23
上傳用戶：lixinxiang
This toolbox was designed as a teaching aid, which matlab is particularly good for since source code is relatively legible and simple to modify. However, it is still reasonably fast if used with the supplied optimiser. However, if you really want to speed things up you should consider compiling the matrix composition routine for H into a mex function. Then again if you really want to speed things up you probably shouldn t be using matlab anyway... Get hold of a dedicated C program once you understand the algorithm.
標簽： particularly designed teaching toolbox
上傳時間： 2016-11-25
上傳用戶：hustfanenze
PRINCIPLE: The UVE algorithm detects and eliminates from a PLS model (including from 1 to A components) those variables that do not carry any relevant information to model Y. The criterion used to trace the un-informative variables is the reliability of the regression coefficients: c_j=mean(b_j)/std(b_j), obtained by jackknifing. The cutoff level, below which c_j is considered to be too small, indicating that the variable j should be removed, is estimated using a matrix of random variables.The predictive power of PLS models built on the retained variables only is evaluated over all 1-a dimensions =(yielding RMSECVnew).
標簽： from eliminates PRINCIPLE algorithm
上傳時間： 2016-11-27
上傳用戶：凌云御清風
The ISD51_Demo project for the MSC1200 shows how to use the ISD51 In-System-Debugger with flash breakpoints or hardware breakpoints. By default, it is configured for flash breakpoints which allow you to set real-time breakpoints in your software. Using Flash breakpoints has also the benefit that no special handing for the shared interrupt vector is required, since the hardware break registers of the MSC1200 are not used at all.
標簽： In-System-Debugger ISD the project
上傳時間： 2014-11-18
上傳用戶：dongqiangqiang
Batch version of the back-propagation algorithm. ％ Given a set of corresponding input-output pairs and an initial network ％ [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the ％ network with backpropagation. ％ ％ The activation functions must be either linear or tanh. The network ％ architecture is defined by the matrix NetDef consisting of two ％ rows. The first row specifies the hidden layer while the second ％ specifies the output layer. ％
標簽： back-propagation corresponding input-output algorithm
上傳時間： 2016-12-27
上傳用戶：exxxds
％ 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
