* 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
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
适用与c5402的DSP编程，c语言编写，经测试，可以直接在ccs下运行。
上传时间： 2014-09-07
上传用户：英雄
isual Chat 1.91 Developer Edition - Customize the Visual Chat code regarding your own requirements - Use customchatdev.html for developing / testing - Create .jar and .cab-files containing client-specific .class-files and the images-folder (use zip and cabarc compressing tools) - Adapt the customchat.html file - Upload all the files to your webserver - Start the ChatServer by invoking java at.ac.uni_linz.tk.vchat.ChatServer [port [server-key]] from your commandline - I kindly ask you to leave copyright and credit information in the InfoPanel.class as it is - but you are invited to add your own text. In case of violations I will consider excluding this class from the source in the future.
标签： Chat requirement Developer Customize
上传时间： 2017-01-27
上传用户：iswlkje
This paper studies the problem of tracking a ballistic object in the reentry phase by processing radar measurements. A suitable (highly nonlinear) model of target motion is developed and the theoretical Cramer—Rao lower bounds (CRLB) of estimation error are derived. The estimation performance (error mean and
标签： processing ballistic the tracking
上传时间： 2014-10-31
上传用户：yyyyyyyyyy
