* 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
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.
标签： recursive prediction algorithm Gauss-Ne
上传时间： 2016-12-27
上传用户：ljt101007
8139 network card linux driver
上传时间： 2016-12-28
上传用户：skhlm
structure EM算法 bayesian network structure learning
标签： structure bayesian learning network
上传时间： 2013-11-27
上传用户：ynsnjs
