Matlab7.x_code

MATLAB和Mathematica、Maple并称为三大数学软件。它在数学类科技应用软件中在数值计算方面首屈一指。MATLAB可以进行矩阵运算、绘制函数和数据、实现算法、创建用户界面、连接其他编程语言的程序等，主要应用于工程计算、控制设计、信号处理与通讯、图像处理、信号检测、金融建模设计与分析等领域。 MATLAB的基本数据单位是矩阵，它的指令表达式与数学、工程中常用的形式十分相似，故用MATLAB来解算问题要比用C，FORTRAN等语言完成相同的事情简捷得多，并且MATLAB也吸收了像Maple等软件的优点，使MATLAB成为一个强大的数学软件。在新的版本中也加入了对C，FORTRAN，C++，JAVA的支持。可以直接调用,用户也可以将自己编写的实用程序导入到MATLAB函数库中方便自己以后调用，此外许多的MATLAB爱好者都编写了一些经典的程序，用户可以直接进行下载就可以用。(MATLAB and Mathematica, Maple and called the three mathematical software. It is during a math class technology application software in the numerical calculations to none. MATLAB matrix operations can be performed, mapping functions and data, algorithm, creating the user interface, connect to other programming languages ​ ​ procedures, mainly used in engineering calculations, control design, signal processing and communications, image processing, signal detection, financial modeling design and analysis and other fields. MATLAB basic unit of data is a matrix, its instruction expression and mathematics, engineering, commonly used forms are very similar, so use MATLAB to solving the problem than with C, FORTRAN and other languages ​ ​ accomplish the same thing more simple, and also MATLAB absorb the advantages of software such as Maple, so MATLAB become a powerful mathematical software. In the new version also adds support for C, FORTRAN, C++, JAVA support. Can be calle)

万哲先的经典版本《代数和编码》，对于编码方面学习具有重要意义。(Zhe-Xian Wan classic version algebra and coding, for the coding to learn of great significance.)

给定极化状态的相位描述子和幅度描述子，绘制该极化状态的poincare功率球。源代码是matlab子函数，输入参数两个：极化状态的幅度描述子和相位描述子。还有一张示例图(Given the polarization state of the phase and amplitude descriptors descriptors, to draw the polarization state poincare power ball. Functions matlab source code, two input parameters: the polarization state and phase amplitude descriptor descriptor.)

Its used for feature generation in matlab

用matlab编写的用来求解四步相移法的光栅相位的程序(This is a matlab prepared for the four-step phase shift method to solve the grating phase program)

k近邻算法和KALMAN滤波相结合的一个实例论文(k nearest neighbor algorithm and the KALMAN filter paper an example of combining)

信号与系统基础知识包括连续信号与模型、离散信号与模型；常用信号变换包括Z变换、Chirp Z变换、FFT变换、DCT变换和Hilbert变换等；离散系统结构包括IIR、FIR和Lattice结构；IIR滤波器设计包括模拟和数字低通、高通、带通与带阻滤波器设计，以及基于冲激响应不变法和双线性Z变换法的IlR滤波器设计等；FIR滤波器设计包括基于窗函数、频率抽样法和切比雪大逼近法的FIR滤波器设计；平稳信号分析包括经典功率谱估计、基于参数模型的功率谱估计和基于非参数模型的功率谱估计；非平稳信号分析包括STFT变换、Gabor展开、Wigner-Ville分布与Choi-Williams分布；非高斯信号分析包括基于非参数法的双谱估计、基于参数模型的双谱估计，以及双谱估计的应用；信号处理的GUI实现包括滤波器设计与分析的FDATool工具和滤波器设计与信号分析的SPTool工具。(signals and systems based on knowledge, including continuous signal with the model, discrete signal and model; Common signals include transform Z transform, chirp Z-transform, FFT transform. DCT and Hilbert transform; Discrete system architecture including IIR, FIR and Lattice structure; IIR filter design, including analog and digital low-pass, high-pass, band-pass and band stop filter design, Based on the impulse response of the same law and bilinear Z transform the IlR filter design; FIR filter design including on the basis of the window function, frequency sampling and snow all over the big approximation of FIR filter design; stationary signal analysis include classical power spectrum estimation, Based on the model parameters of the power spectrum estimation and non- parametric model b)

BP神经网络 BP神经网络 BP神经网络 BP神经网络 BP神经网络( BP neural network BP neural network BP neural network)

分形的练习一 ①Koch曲线 用复数的方法来迭代Koch曲线 clear i 防止i被重新赋值 A=[0 1] 初始A是连接（0，0）与（1，0）的线段 t=exp(i*pi/3) n=2 n是迭代次数 for j=0:n A=A/3 a=ones(1,2*4^j) A=[A (t*A+a/3) (A/t+(1/2+sqrt(3)/6*i)*a) A+2/3*a] end plot(real(A),imag(A)) axis([0 1 -0.1 0.8]) ②Sierpinski三角形 A=[0 1 0.5 0 0 1] 初始化A n=3 迭代次数 for i=1:n A=A/2 b=zeros(1,3^i) c=ones(1,3^i)/2 A=[A A+[c b] A+[c/2 c]] end for i=1:3^n patch(A(1,3*i-2:3*i),A(2,3*i-2:3*i), b ) patch填充函数 end (Fractal Exercise One The ① Koch curve Plural iteration Koch curve clear i to prevent i is reassigned A = [0 1] initial A is a connection (0,0) and (1,0) of the segments t = exp (i* pi/3) n = 2 n is the number of iterations for j = 0: n A = A/3 a = ones (1,2* 4 ^ j) A = [A (t* A+ a/3) (A/t+ (1/2+ sqrt (3)/6* i)* a) A+2/3* a] end plot (real (A), imag (A)) axis ([0 1-0.1 0.8])   ② Sierpinski triangle A = [0 1 0.5 0 0 1] initialized A n = 3 the number of iterations. for i = 1: n A = A/2 b = zeros (1,3 ^ i) c = ones (1,3 ^ i)/2 A = [A A+ [c b] A+ [c/2 c]] end for i = 1:3 ^ n patch (A (1,3* i-2: 3* i), A (2,3* i-2: 3* i), b ) patch filled function end)

电力系统潮流分析计算14节点信息提取雅克比矩阵等(Power flow analysis and calculation 14 nodes Jacobi matrix and other information extraction)