ISING2D

用Gauss_Seidel迭代法来求解方程组的解的程序(With Gauss_Seidel iterative method to solve the solution of system program)

BEZIER_SURFACE_DISPLAY is a work in progress . I have managed to read the data in and display a crude image, in which each Bezier patch is represented by a flat rectangle. Obviously, I need to take the next step, which is to be able to sample the bicubic function and display a finer mesh of rectangles, in order to see the teapot in its glory!

automated initialization of inverted pendulum parameters

在系统辨识中，应用多级最小二乘可以很好的估计出噪声系数(In system identification, the application can be multi-stage least squares estimates of the noise figure)

主要是关于经验模态分解的还有EEMD的代码(Mainly on empirical mode decomposition EEMD code there)

提供了使用matlab仿真2FSK误码率的源程序，可供初学者学习参考。(Provided the use of matlab simulation BER 2FSK source for beginners to learn the reference.)

说明：  这是用小波变换模极大值重建信号的源程序，数据是一心电信号，在matlab6。5下实现，来源于胡广书的《现代信号处理教程》附属光盘，现提供给大家供大家学习参考，滤波部分可以根据个人情况进行修改。程序包含四个部份，其中wave_peak实现信号的分解并求出模极大值(This is the wavelet transform modulus maxima reconstruction of the source signal, data signal is bent in matlab6. 5 under, and from the HU-book "modern signal processing Guide" CD subsidiary, it is available to everyone for your study reference, filtering can be part of the personal changes . Procedures include four parts, with wave_peak signal decomposition and sought modulus maxima)

均匀球形粒子的Mie理论散射光强计算程序： 1．RB1.m 计算第一类Ricatti-Bessel函数的函数 2．RB2.m 计算第二类Ricatti-Bessel函数的函数 3. Alegendr.m 计算角函数 和 的函数 4. MieCoeff.m 计算米尔系数的函数 5. Amp.m 计算散射光复振幅的函数 6. MieIntensity.m 画均匀粒子散射角-光强图 (Uniform spherical particle Mie theory scattering intensity calculation procedures: 1. RB1.m calculate the first class Ricatti-Bessel function of the function 2. RB2.m calculate the second category Ricatti-Bessel function of the function 3. Alegendr.m computing angle functions and function 4. MieCoeff.m Mill coefficient calculation function 5. Amp.m calculate the scattering amplitude recovery function 6. MieIntensity.m Painting uniform particle scattering angle- light intensity diagram)

fastICA,进行串行盲源分离的一种不错的方法(fastICA, serial blind source separation method for a good)

说明：  基于主成分分析实现对特征脸的人脸识别，训练，输出识别率等。(Based on principal component analysis to achieve the characteristics of face recognition, training, recognition rate of the output.)