This document accompanies a sample co-installer that can be used in conjunction with an INF file to install additional device INF files on the target system during a device installation. The instructions herein apply to the Microsoft Windows 2000 and Windows XP and Windows Server 2003 operating systems. The sample co-installer described in this article interprets CopyINF directives in a [DDInstall] section in an INF file. The sample demonstrates using a co-installer to perform processing after a device has been installed, parsing the INF section that is being used for the installation, and the use of the SetupCopyOEMInf, SetupGetInfInformation, SetupQueryInfOriginalFileInformation and SetupDiGetActualSectionToInstall APIs.
标签： co-installer accompanies conjunction document
上传时间： 2014-02-28
上传用户：gououo
This tutorial white-paper illustrates practical aspects of FIR filter design and fixed-point implementation along with the algorithms available in the Filter Design Toolbox and the Signal Processing Toolbox for this purpose.
标签： illustrates fixed-point white-paper practical
上传时间： 2016-12-14
上传用户：15071087253
* Lightweight backpropagation neural network. * This a lightweight library implementating a neural network for use * in C and C++ programs. It is intended for use in applications that * just happen to need a simply neural network and do not want to use * needlessly complex neural network libraries. It features multilayer * feedforward perceptron neural networks, sigmoidal activation function * with bias, backpropagation training with settable learning rate and * momentum, and backpropagation training in batches.
标签： backpropagation implementating Lightweight lightweight
上传时间： 2013-12-27
上传用户：清风冷雨
It is a GPL basic windowing library created specifically for windows and uses only basic win32 services. It currently compiles under Borland C++ and Microsoft C++, other compilers are untested.It provides a common windows toolkit for al c++ environments.
标签： basic specifically windowing created
上传时间： 2016-12-20
上传用户：hgy9473
sqlite的帮助文档， This ZIP archive contains most of the static HTML files that comprise this website, including all of the SQL Syntax and the C/C++ interface specs and other miscellaneous documentation.
上传时间： 2013-12-23
上传用户：evil
java中大部分对图形、文本、图像的操作方法都定义在Graphics类中，所以此次实验使用的方法如Color(int r, int g,int b), setColor(Color c),drawline(int x1,int y1,int x2,int y2)等都来自Graphics类中，此外对文本和字体的处理还用到了Font类中的 new Font(“字体名”，字体风格，字体大小)，setFont（Font f）等方法；
上传时间： 2013-11-29
上传用户：yimoney
This function calculates Akaike s final prediction error ％ estimate of the average generalization error. ％ ％ [FPE,deff,varest,H] = fpe(NetDef,W1,W2,PHI,Y,trparms) produces the ％ final prediction error estimate (fpe), the effective number of ％ weights in the network if the network has been trained with ％ weight decay, an estimate of the noise variance, and the Gauss-Newton ％ Hessian. ％
标签： generalization calculates prediction function
上传时间： 2014-12-03
上传用户：maizezhen
％ 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 calculates Akaike s final prediction error ％ estimate of the average generalization error for network ％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive ％ counterparts. ％ ％ [FPE,deff,varest,H] = nnfpe(method,NetDef,W1,W2,U,Y,NN,trparms,skip,Chat) ％ produces the final prediction error estimate (fpe), the effective number ％ of weights in the network if it has been trained with weight decay, ％ an estimate of the noise variance, and the Gauss-Newton Hessian. ％
标签： generalization calculates prediction function
上传时间： 2016-12-27
上传用户：脚趾头
It has been suggested1 that an appropriate figure of merit for a low probability of intercept and detection (LPI/D) waveform is the quantity “Range x Bandwidth / Joule”. That is, the further the range, the wider the bandwidth and the less amount of energy used to achieve these values, the more covert is the resultant communications system.
标签： appropriate probability suggested1 intercept
上传时间： 2017-01-03
上传用户：kr770906
