PID

this program is the simulink of logistic map

full implementation to LDA with Minumum distance Classifier for face recognition and easy to implement.

create train data untuk membuat data training1

這是一個 Matlab的（和獨立應用）端口的出色的機器學習算法，隨機森林` - 由Leo布賴曼等。從 R -源由Andy鴻等。 http://cran.r-project.org/web/packages/randomForest/index.html（Fortran的原由Leo布賴曼和Adele卡特勒，研究港口安迪鴻和馬修維納。）當前的代碼版本是基於 4.5-29從來源 randomForest包。(This is a Matlab (and Standalone application) port for the excellent machine learning algorithm `Random Forests - By Leo Breiman et al. from the R-source by Andy Liaw et al. http://cran.r-project.org/web/packages/randomForest/index.html ( Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener.) Current code version is based on 4.5-29 from source of randomForest package.)

说明：  20套+精华图标+等级图标+置顶图标+打包下载(20 sets+ essence+ rating icon icon icon+ package+ Top Download)

抽样定理演示程序，能清楚看到当抽样速率大于2fs时恢复模拟信号的过程和当抽样频率小于2fs时频率混叠的过程。同时还包括频率抽样过程的演示。非常详尽。通过此演示程序，你肯定能明白抽样定理这一通信领域基本的原理！(The sampling theorem demo program, you can clearly see that when the recovery process of the analog signal sampling rate greater than 2fs and frequency aliasing when the sampling frequency is less than 2fs process. Also includes a demonstration of the frequency of the sampling process. Very detailed. Through this demonstration program, you can certainly understand sampling theorem this basic principle in the field of communication!)

Elman神经网络：Elman网络是 J. L. Elman于1990年首先针对语音处理问题而提出来的, 它是一种典型的局部回归网络( global feed for ward l ocal recurrent)。Elman网络可以看作是一个具有局部记忆单元和局部反馈连接的前向神经网络。Elman网络具有与多层前向网络相似的多层结构。它的主要结构是前馈连接, 包括输入层、 隐含层、 输出层, 其连接权可以进行学习修正 反馈连接由一组“结构 ” 单元构成,用来记忆前一时刻的输出值, 其连接权值是固定的。在这种网络中, 除了普通的隐含层外, 还有一个特别的隐含层,称为关联层 (或联系单元层 ) 该层从隐含层接收反馈信号, 每一个隐含层节点都有一个与之对应的关联层节点连接。关联层的作用是通过联接记忆将上一个时刻的隐层状态连同当前时刻的网络输入一起作为隐层的输入, 相当于状态反馈。隐层的传递函数仍为某种非线性函数, 一般为 Sigmoid函数, 输出层为线性函数, 关联层也为线性函数。(Elman neural network: Elman JL Elman network is first proposed for speech processing problems in 1990. It is a typical local regression network (global feed for ward l ocal recurrent). Elman network can be seen as having a forward neural network of local memory unit and local feedback connections. Elman network has a multi-layer multilayer structure similar to the network. Its main structure is a feedforward connection, comprising an input layer, a hidden layer and output layer, which can be connected right learning correction feedback connected by a set of " structural" units, the output value of the memory for the previous time, which connection weights are fixed. In such a network, in addition to ordinary hidden layer, there is a special hidden layer, called an associative layer (or contact layer unit) this layer receives feedback signals the hidden layer, each hidden layer node has an associated layer node corresponding connection. The role of the associated layer is fo)

代码汇总了几种不同的MIMO系统中注水算法，适合初学者学习仿真(“Water_Pouring” for water-pouring algorithm)

Wireless Sensor Networks WSNs Modified LEACH P=0.1

This code is about radon transform of seismic data.