GaussianMixtureModel_2-

这是一本介绍matlab的电子书籍，内容较全面，比较易懂，适合matlab初学者使用。(This is a description of electronic books matlab, content more comprehensive, easier to understand for beginners to use matlab)

说明：  在matlab中开发了一种如何使算法收敛更快，精度更高的算法，效果明显(developed in the Matlab how a convergence algorithm faster and more precise algorithms, obviously)

Image Fusion Techniques.

基于小波变换的图象去噪 Normalshrink算法(based on wavelet transform algorithm for image denoising Normalshrink)

胡广书源代码，《现代信号处理》比较实用，总体不错(Hu Guang-shu source code)

PSO算法和最速下降法混合算法，求解单目标优化问题，测试函数是非线性方程组，解决MinF(x)的问题。(PSO algorithm and steepest descent method hybrid algorithm for solving single-objective optimization problem, the test function is non-linear equations, solving MinF (x) of the problem.)

压缩包中的程序用于局部放电的模式识别，里面包含去噪，特征提取及模式识别程序。(Compressed package in the program for partial discharge pattern recognition, which contains denoising, feature extraction and pattern recognition procedures)

说明：  用于提取语音每帧的mfcc函数，将数据保存在cell文件中，可以直接使用cell，也可经过变换，提取到表格中使用(The MFCC function used to extract each frame of speech is saved in cell file. Cell can be used directly or transformed to extract the data into tables)

介绍了SVM的基础知识，包括：SVM入门（一）SVM简介；SVM入门（二）线性分类器part1；SVM入门（三）线性分类器part2；SVM入门（四）线性分类器的求解—问题的描述part1；SVM入门（五）线性分类器的求解—问题的描述part2等。(Describes the basics of SVM, including: SVM Getting Started (1) SVM Introduction SVM Getting Started (2) linear classifier part1 SVM Getting Started (3) linear classifier part2 SVM entry (d) the solution of a linear classifier- description of the problem part1 SVM Getting Started (5) the solution of the linear classifier- description of the problem part2 and so on.)

This simulink model simulates the damped driven pendulum, showing it s chaotic motion. theta = angle of pendulum omega = (d/dt)theta = angular velocity Gamma(t) = gcos(phi) = Force omega_d = (d/dt) phi Gamma(t) = (d/dt)omega + omega/Q + sin(theta) Play with the initial conditions (omega_0, theta_0, phi_0 = omega(t=0), theta(t=0), phi(t=0)) and the system parameters (g, Q, omega_d) and the solver parameters/method. Chaos can be seen for Q=2, omega_d=w/3. The program outputs to Matlab time, theta(time) & omega(time). Plot the phase space via: plot(mod(theta+pi, 2*pi)-pi, omega, . ) Plot the Poincare sections using: t_P = (0:2*pi/omega_d:max(time)) plot(mod(spline(time, theta+pi, t_P), 2*pi)-pi, spline(time, omega, t_P), . ) System is described in: "Fractal basin boundaries and intermittency in the driven damped pendulum" E. G. Gwinn and R. M. Westervelt PRA 33(6):4143 (1986) (This simulink model simulates the damped driven pendulum, showing it s chaotic motion. theta = angle of pendulum omega = (d/dt)theta = angular velocity Gamma(t) = gcos(phi) = Force omega_d = (d/dt) phi Gamma(t) = (d/dt)omega+ omega/Q+ sin(theta) Play with the initial conditions (omega_0, theta_0, phi_0 = omega(t=0), theta(t=0), phi(t=0)) and the system parameters (g, Q, omega_d) and the solver parameters/method. Chaos can be seen for Q=2, omega_d=w/3. The program outputs to Matlab time, theta(time) & omega(time). Plot the phase space via: plot(mod(theta+pi, 2*pi)-pi, omega, . ) Plot the Poincare sections using: t_P = (0:2*pi/omega_d:max(time)) plot(mod(spline(time, theta+pi, t_P), 2*pi)-pi, spline(time, omega, t_P), . ) System is described in: "Fractal basin boundaries and intermittency in the driven damped pendulum" E. G. Gwinn and R. M. Westervelt PRA 33(6):4143 (1986) )