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.
標簽： Programmer Networks entirely network
上傳時間： 2013-12-24
上傳用戶：liuchee
NEURAL network 一個演示原理的代碼，便于初學者學習。
上傳時間： 2013-12-25
上傳用戶：181992417
the load forecast based on NEURAL Networks,use the MATLAB
標簽： the forecast Networks NEURAL
上傳時間： 2016-12-21
上傳用戶：iswlkje
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. ％
標簽： ahead predictions determined simulation
上傳時間： 2016-12-27
上傳用戶：busterman
％ 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
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.
標簽： function strategy Optimal Surgeon
上傳時間： 2013-12-19
上傳用戶：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.
標簽： recursive prediction algorithm Gauss-Ne
上傳時間： 2016-12-27
上傳用戶：ljt101007
hopfield NEURAL network
上傳時間： 2014-01-20
上傳用戶：釣鰲牧馬
《神經網絡設計》(NEURAL networks design)的源代碼
標簽： networks NEURAL design 神經網絡
上傳時間： 2013-12-18
上傳用戶：wfeel
Kalman Filtering and NEURAL Networks - Simon Haykin 原來網上的文件缺少第五章，這個完整版，歡迎下載，英文牛B的人可以翻譯好后，再上傳，謝謝！
標簽： Filtering Networks Kalman Haykin
上傳時間： 2017-02-01
上傳用戶：標點符號
