CMA

说明：  这也是一个对初学者有用的MATLAB源码，希望对你有用！( This is also one to the beginner useful MATLAB sound code, hoped that is useful to you! )

说明：  该程序是使用matlab编写的支持向量机程序，它可用于分类/预测等方面。(it is very useful!)

说明：  利用随机方法产生-1 +1 分布的白噪声(using random methods-1 1 distribution of white noise)

matlab因子分析的几个例程 包括：基于协方差矩阵或相关矩阵系数矩阵的因子分析 ，与基于样本观测值矩阵的因子分析(matlab factor analysis of several routines include: factor covariance matrix or correlation matrix coefficient matrix-based analysis, and factor sample observations Matrix Analysis)

pwm整流器的建模仿真，一些自适应信号处理的算法，包含位置式PID算法、积分分离式PID，使用matlab实现智能预测控制算法，实现串口的数据采集，双向PCS控制仿真。( Modeling and simulation pwm rectifier Some adaptive signal processing algorithms, It contains positional PID algorithm, integral separate PID, Use matlab intelligent predictive control algorithm, Achieve serial data acquisition, Two-way PCS control simulation.)

此函数用于IS-95前向链路系统的仿真，包括扩频调制，匹配滤波，RAKE接收等相关通信模块。仿真环境: 加性高斯白噪声信道. (This function is used to forward link IS-95 system simulation, including the spread spectrum modulation, matched filter, RAKE receiver, and other related communication module. Simulation environment: additive white Gaussian noise channel.)

倒立摆的数学建模以及各种控制的方案与实验结果(Inverted pendulum control of mathematical modeling and a variety of programs and experimental results)

将MATLM3应用于有限差分法的正演计算中．充分发挥了其强大而方便的功能。 通过对二维稳定电流场模型的试算表明，ATLAB在解决实际的工程和数学问题中，与其它计算机程序设计语言 FORTRAN相比，具有使用更为简便、语句功能更强(Will MATLM3 finite difference method applied to the forward calculation. Give full play to its powerful and convenient features. Through the two-dimensional current field model calculations show that, ATLAB in solving practical engineering and mathematical problems, and other computer programming language FORTRAN, compared with the use of more convenient, more powerful statement)

Numerical Methods of Exploration Seismology crewes小组的matlab层析成像说明文档(Numerical Methods of Exploration Seismology crewes team matlab tomography documentation)

clear all clc t=0:1/1000:10-1/1000 s=sin(2*pi*t) snr=20 s_power=var(s) varience of s linear_snr=10^(snr/10) factor=sqrt(s_power/linear_snr) noise=randn(1,length(s))*factor x=s+noise Ó É SNR¼ Æ Ë ã Ë æ » úÔ ë É ù x1=noise Ô ë É ùÔ ´ Ê ä È ë x2=noise w1=0 È ¨Ï µ Ê ý ³ õ Ö µ w2=0 e=zeros(1,length(x)) y=0 u=0.05 for i=1:10000 LMSË ã ·¨ y(i)=w1*x1(i)+w2*x2(i) e(i)=x(i)-y(i) w1=w1+u*e(i)*x1(i) w2=w2+u*e(i)*x2(i) end figure(1) subplot(4,1,1)