員工培訓系統(tǒng) 首先確認您的機器上已經(jīng)安裝了VC6.0以上版本，要編譯生成可執(zhí)行文件，需打開后綴名為dsw的文件，系統(tǒng)會默認用VC打開，然后選擇Build菜單下的Set Active Configuration選項，選擇Project Configuration為Win32 Release，然后編譯項目，得到可執(zhí)行文件。連接數(shù)據(jù)庫請參照第2章的方法，在ODBC數(shù)據(jù)源內(nèi)添加Microsoft Access數(shù)據(jù)庫TRAIN.mdb，并將數(shù)據(jù)源名設(shè)定為TRAIN即可實現(xiàn)數(shù)據(jù)庫和應(yīng)用程序的正常連接，程序才能正常實現(xiàn)數(shù)據(jù)庫的訪問。另外，如果用戶自己編寫了.chm的幫助文檔只要和可執(zhí)行文件放在同一個目錄下就可以了。 用戶要修改程序源碼可以選擇相應(yīng)的.h和.cpp文件，本實例使用的命名規(guī)則基本是：DIALOG_***** 為管理對話框資源， *****Info為數(shù)據(jù)輸入窗口資源， *****Set為結(jié)果集對應(yīng)的類
上傳時間： 2014-01-03
上傳用戶：luopoguixiong
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to TRAIN a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
標簽： Rauch-Tung-Striebel algorithm smoother which
上傳時間： 2016-04-15
上傳用戶：zhenyushaw
JaNet: Java Neural Network Toolkit resume: A well documented toolkit for designing and TRAINing, and a java library for inclusion in third party programs. description: jaNet package is a java neural network toolkit, which you can use to design, test, TRAIN and optimize an ideal Neural Network for your private application. You can then include your saved network in your program using the jaNet.backprop package. The consequent documentation is only in french for the moment, but an english translation is planned. The java source code is released under GPL, and can be compiled with JDK, Symantec Cafe or MS Visual J
標簽： documented designing TRAINing Network
上傳時間： 2016-04-15
上傳用戶：zhanditian
神經(jīng)網(wǎng)絡(luò)的基本介紹，包括了由工具箱指定的有關(guān)網(wǎng)絡(luò)結(jié)構(gòu)和符號的基本材料以及建立神經(jīng)網(wǎng)絡(luò)的一些基本函數(shù)，例如new、init、adapt和TRAIN。以反向傳播網(wǎng)絡(luò)為例講解了反向傳播網(wǎng)絡(luò)的原理和應(yīng)用的基本過程。
上傳時間： 2013-12-16
上傳用戶：奇奇奔奔
這是讀好的ORL 和YALE人臉庫數(shù)據(jù)， 用LODA加載后， 變量TRAIN 代表是訓練樣本， test 代碼是測試樣本。
標簽： YALE ORL 人臉 數(shù)據(jù)
上傳時間： 2014-01-04
上傳用戶：zhangyigenius
Face Recognition Library ======================== Advanced face recognition DLL using two functions : TRAIN and Recognize. Uses neural net back propogation alogorithm with more AI tools added for imaging optimization. Library works great even for a low resolution web cam image and requires the user to align to a mirror frame on screen. Complete Source Code with Video capture and feature extraction kit for Registered Users. Please register here for only $299 with Source Code : http://www.research-lab.com/facerecognitionorder.htm (c) www.research-lab.com
標簽： Recognition recognition Advanced Library
上傳時間： 2017-04-25
上傳用戶：784533221
matlab神經(jīng)網(wǎng)絡(luò)工具箱的實用指南，第一章是神經(jīng)網(wǎng)絡(luò)的基本介紹，第二章包括了由工具箱指定的有關(guān)網(wǎng)絡(luò)結(jié)構(gòu)和符號的基本材料以及建立神經(jīng)網(wǎng)絡(luò)的一些基本函數(shù)，例如new、init、adapt和TRAIN。第三章以反向傳播網(wǎng)絡(luò)為例講解了反向傳播網(wǎng)絡(luò)的原理和應(yīng)用的基本過程。
標簽： matlab 神經(jīng)網(wǎng)絡(luò) 工具箱
上傳時間： 2017-05-07
上傳用戶：zhyiroy
人工神經(jīng)網(wǎng)絡(luò)分類實現(xiàn)，在vs2005下實現(xiàn)，訓練數(shù)據(jù)和測試數(shù)據(jù)有TRAIN.txt和test.txt讀入
標簽： 人工神經(jīng)網(wǎng)絡(luò) 分類
上傳時間： 2017-05-21
上傳用戶：洛木卓
svm中TRAIN方法，適用于做svm分類的用戶直接調(diào)用
標簽： svm
上傳時間： 2015-05-05
上傳用戶：saberxun
% 生成訓練樣本集 clear all; clc; P=[110 0.807 240 0.2 15 1 18 2 1.5; 110 2.865 240 0.1 15 2 12 1 2; 110 2.59 240 0.1 12 4 24 1 1.5; 220 0.6 240 0.3 12 3 18 2 1; 220 3 240 0.3 25 3 21 1 1.5; 110 1.562 240 0.3 15 3 18 1 1.5; 110 0.547 240 0.3 15 1 9 2 1.5]; 0 1.318 300 0.1 15 2 18 1 2]; T=[54248 162787 168380 314797; 28614 63958 69637 82898; 86002 402710 644415 328084; 230802 445102 362823 335913; 60257 127892 76753 73541; 34615 93532 80762 110049; 56783 172907 164548 144040]; @907 117437 120368 130179]; m=max(max(P)); n=max(max(T)); P=P'/m; T=T'/n; %-------------------------------------------------------------------------% pr(1:9,1)=0; %輸入矢量的取值范圍矩陣 pr(1:9,2)=1; bpnet=newff(pr,[12 4],{'logsig', 'logsig'}, 'TRAINgdx', 'learngdm'); %建立BP神經(jīng)網(wǎng)絡(luò)， 12個隱層神經(jīng)元，4個輸出神經(jīng)元 %tranferFcn屬性 'logsig' 隱層采用Sigmoid傳輸函數(shù) %tranferFcn屬性 'logsig' 輸出層采用Sigmoid傳輸函數(shù) %TRAINFcn屬性 'TRAINgdx' 自適應(yīng)調(diào)整學習速率附加動量因子梯度下降反向傳播算法訓練函數(shù) %learn屬性 'learngdm' 附加動量因子的梯度下降學習函數(shù) net.TRAINParam.epochs=1000;%允許最大訓練步數(shù)2000步 net.TRAINParam.goal=0.001; %訓練目標最小誤差0.001 net.TRAINParam.show=10; %每間隔100步顯示一次訓練結(jié)果 net.TRAINParam.lr=0.05; %學習速率0.05 bpnet=TRAIN(bpnet,P,T); %------------------------------------------------------------------------- p=[110 1.318 300 0.1 15 2 18 1 2]; p=p'/m; r=sim(bpnet,p); R=r'*n; display(R);
上傳時間： 2016-05-28
上傳用戶：shanqiu
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