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Description: 基于Fase 4 RDF/RSS语法分析器，Feed是向智能信息和消息检索代理迈出的第一步。它使用一个普通文本文件作为存储，而没有使用任何种类的数据库，应该可以在任何支持PHP的站点工作-on Fase 4 RDF / RSS parser, Feed is to intelligent information retrieval and news agency as a first step. It uses an ordinary text files as storage, but did not use any kind of database and should be able to support any of the PHP site work
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Size: 509772 |
Author: 侯为 |
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Description: 基于Fase 4 RDF/RSS语法分析器，Feed是向智能信息和消息检索代理迈出的第一步。它使用一个普通文本文件作为存储，而没有使用任何种类的数据库，应该可以在任何支持PHP的站点工作-on Fase 4 RDF/RSS parser, Feed is to intelligent information retrieval and news agency as a first step. It uses an ordinary text files as storage, but did not use any kind of database and should be able to support any of the PHP site work
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Size: 509952 |
Author: 侯为 |
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Description: 本程序为一个误差向后传播的三层前馈神经网络有指导的学习算法,用户可在文件中设置相应参数.-this program back to an error in transmission feed-forward neural network algorithm for the study guide, users can set up in the document corresponding parameters.
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Size: 475136 |
Author: 彭斌 |
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Description: Feed Forward Neural Network 例子-Feed Forward Neural Network examples
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Size: 4096 |
Author: |
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Description: 利用ajax技术及javascript语言编写的rss阅读器，可以读本地的xml文档和外网上的feed。考虑了两种浏览器（IE和火狐），在Eclipse开发工具上编写-Ajax and javascript to use the technical language of rss readers can read the xml document local and foreign online feed. Consider two types of browsers (IE and Firefox), in the Eclipse development tool to prepare
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Size: 623616 |
Author: 蔡圆 |
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Description: bp神经网络算法是解决最优化问题的先进算法之一，本论文讨论了神经网络中使用最为广泛的前馈神经网络。其网络权值学习算法中影响最大的就是误差反向传播算法（back-propagation简称BP算法）。BP算法存在局部极小点，收敛速度慢等缺点。基于优化理论的Levenberg-Marquardt算法忽略了二阶项。该文讨论当误差不为零或者不为线性函数即二阶项S(W)不能忽略时的Hesse矩阵的近似计算，进而训练网络。-bp neural network algorithm to solve optimization problems, one of the advanced algorithm, the paper discusses the neural network in the most widely used feed-forward neural network. Its network weights learning algorithm in the greatest impact is the error back-propagation algorithm (back-propagation algorithm referred to as BP). BP algorithm for the existence of local minimum points, such as the shortcomings of slow convergence. Optimization theory based on the Levenberg-Marquardt algorithm ignores the second-order item. In this paper, the discussion when the error is not zero or not that is second-order linear function of S (W) can not be ignored when the Hesse matrix of approximate calculation, and then training the network.
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Size: 19456 |
Author: 刘慧 |
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Description: 基于RBF神经网络整定的PID控制
径向基函数具有单隐居的三层前馈网络。是—种局部逼近网络，己证明它能以任意精度逼近杠意连续函数。
-RBF neural network based tuning of PID control of radial basis function with the seclusion of the three single-feed-forward network. Yes- different local approximation network, it has been proved to arbitrary precision bars intended continuous function approximation.
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Size: 1024 |
Author: feiyang |
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Description: 三层前向神经网络的权值训练方法
变步长的bp法
-Three feed-forward neural network training method of the right to change the value of the bp-step method
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Size: 5120 |
Author: dongenzeng |
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Description: 三层前馈神经网络的BP算法。程序具有以下功能：
（1） 允许选择各层节点数；
（2） 允许选用不同的学习率η；
（3） 能对权值进行初始化，初始化用[-1、1]区间的随机数；
（4）允许选用单极性和双极性两种不同Sigmoid型转移函数。
-Three-tier feed-forward neural network BP algorithm. Procedures have the following functions: (1) allows to choose the number of nodes on each floor (2) allows selection of different learning rate η (3) be able to initialize the weights, initialized by [-1,1] interval random number (4) to allow selection of unipolar and bipolar type two different Sigmoid transfer function.
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Size: 1024 |
Author: Mingruixia |
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Description: Liferea is an abbreviation for Linux Feed Reader. It is a news
aggregator for online news feeds. It supports a number of different
feed formats including RSS/RDF, CDF and Atom. There are many other
news readers available, but these others are not available for
Linux or require many extra libraries to be installed. Liferea
tries to fill this gap by creating a fast, easy to use, easy to
install news aggregator for GTK/GNOME.
It was last updated for Liferea version 1.4.
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Size: 1615872 |
Author: greatho |
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Description: Delphi DirectX9, control joystick with directXinput and force feed back
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Size: 879616 |
Author: c4_esp |
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Description: Multi level feed back q-Multi level feed back queue
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Size: 1024 |
Author: Pro123 |
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Description: matlab动量梯度下降算法
生成一个新的前向神经网络
对BP神经网络进行训练
对BP神经网络进行仿真-Momentum matlab gradient descent algorithm to generate a new feed-forward neural networks trained BP neural network on the BP neural network simulation
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Size: 1024 |
Author: 舒 |
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Description: NN neural network for face recognition
supervised feed forward back propagation neural network
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Size: 1024 |
Author: mony |
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Description: simple feed forward multilayer network
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Size: 1024 |
Author: shahrani shahbudin |
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Description: Perceptron LMS Feed Forward Back Propagation Character Recognition
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Size: 14336 |
Author: ssss |
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Description: Visual Basic DirectX9, control joystick with directXinput and force feed back
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Size: 987136 |
Author: c4_esp |
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Description: feed 网站的feed，php 文件
feed 网站的feed，php 文件-feed
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Size: 5158912 |
Author: wuc |
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Description: RNNSIM ver. 1.0 is a program with an intercative graphical user interface
(GUI) that runs under MATLAB ver. 5.0 or higher. The program can be used
in training and testing the Random Neural Network(RNN) models.
This version (ver. 1.0) implements only the 3 layer feed forward RNN model.
In the next versions, the multi hidden layers and the recurrent RNN models
can be implemented. To obtain faster training, the training section can be
written as a MEX file and invoked from the GUI.
If you have the m files in the directory rnnsim for example, then you can
run the program following the next steps:
1- run MATLAB as usual
2- from the MATLAB command window, write cd rnnsim
3- from the MATLAB command window, write rnnsim- RNNSIM ver. 1.0 is a program with an intercative graphical user interface
(GUI) that runs under MATLAB ver. 5.0 or higher. The program can be used
in training and testing the Random Neural Network(RNN) models.
This version (ver. 1.0) implements only the 3 layer feed forward RNN model.
In the next versions, the multi hidden layers and the recurrent RNN models
can be implemented. To obtain faster training, the training section can be
written as a MEX file and invoked from the GUI.
If you have the m files in the directory rnnsim for example, then you can
run the program following the next steps:
1- run MATLAB as usual
2- from the MATLAB command window, write cd rnnsim
3- from the MATLAB command window, write rnnsim
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Size: 63488 |
Author: hacen |
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Description: THE REGULATOR OF SPEED OF DOUBLE FEED INDUCTION GENERATOR
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Size: 6144 |
Author: kara |
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