Canny边缘检测的matlab

matlab和simulink的进阶书籍，适合控制类专业的学生(Advanced matlab and simulink books suitable for the control class professional students)

将角度和幅度的二维数组画成dB值的polar图( POLARDB Polar coordinate plot. POLARDB(THETA, RHO) makes a plot using polar coordinates of the angle THETA, in radians, versus the radius RHO in dB. The maximum value of RHO should not exceed 1. It should not be normalized, however (i.e. its max. value may be less than 1). POLAR(THETA,RHO,S) uses the linestyle specified in string S. See PLOT for a description of legal linestyles. )

simulink仿真框图来实现BPSK调制(simulation simulink block diagram to realize BPSK modulation)

usaging of Java Spinner controls in Matlab

说明：  讲解OFDM无线系统同步方案。提出了两种同步方案。(Master s thesis about The methods of OFDM synchronization )

这是很全面偏最小二乘法程序,并且具有中文注释，有效帮助大家利用PLS算法。(It is program of Partial least square.It is noted by chinese.)

frequency hopping soucce code

一个用于识别客车、轿车、面包车等的车型识别的程序，用matlab编写，利用背景差分得到车辆区域，再做后续图像处理(A model identification procedures used to identify buses, cars, vans, etc., using matlab prepared using the context of the differential area of ​ ​ the vehicle, then a subsequent image processing)

基于单神经元网络方法的等温模锻过程PID控制算法。(PID control algorithm based on isothermal forging process of a single neural network approach.)

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)