1-1-1 neural network
上传时间: 2014-01-21
上传用户:zhanditian
using delta rule neural network
标签: network neural using delta
上传时间: 2014-12-22
上传用户:talenthn
根据微软权威的软件开发指南MSDN(Microsoft Developer Network)的定义,ActiveX插件以前也叫做OLE控件或OCX控件,它是一些软件组件或对象,可以将其插入到WEB网页或其它应用程序中。
标签: Developer Microsoft Network MSDN
上传时间: 2013-12-25
上传用户:xfbs821
* 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
neural network utility is a Neural Networks library for the C++ Programmer. It is entirely object oriented and focuses on reducing tedious and confusing problems of programming neural networks. By this I mean that network layers are easily defined. An entire multi-layer network can be created in a few lines, and trained with two functions. Layers can be connected to one another easily and painlessly.
标签: Programmer Networks entirely network
上传时间: 2013-12-24
上传用户:liuchee
neural network 一个演示原理的代码,便于初学者学习。
上传时间: 2013-12-25
上传用户:181992417
k-step ahead predictions determined by simulation of the % one-step ahead neural network predictor. For NNARMAX % models the residuals are set to zero when calculating the % predictions. The predictions are compared to the observed output. %
标签: ahead predictions determined simulation
上传时间: 2016-12-27
上传用户:busterman
Produces a matrix of derivatives of network output w.r.t. % each network weight for use in the functions NNPRUNE and NNFPE.
标签: network w.r.t. derivatives Produces
上传时间: 2013-12-18
上传用户:sunjet
% 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 applies the Optimal Brain Surgeon (OBS) strategy for % pruning neural network models of dynamic systems. That is networks % trained by NNARX, NNOE, NNARMAX1, NNARMAX2, or their recursive % counterparts.
标签: function strategy Optimal Surgeon
上传时间: 2013-12-19
上传用户:ma1301115706