ARW

matlab编程的电子书籍。对想学习matlab的同学比较有用(matlab programming e-books. Useful to the students want to learn matlab)

Some codes for the solutions of the book Fundamentals of Photonics

Power Systems Oscillations C4 examples

用Jacobi Gauss-seidel SOR迭代法求解线性方程组(With Jacobi Gauss-seidel SOR iterative method for solving linear equations)

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) )

最小二乘回归法（PLSR：partial least squares regression)：是一种新型的多元统计数据分析方法，它主要研究的是多因变量对多自变量的回归建模，特别当各变量内部高度线性相关时，用偏最小二乘回归法更有效。另外，偏最小二乘回归较好地解决了样本个数少于变量个数等问题。(Least squares regression (PLSR: partial least squares regression): a new type of multivariate statistical data analysis methods, the main research is more dependent variable regression modeling of multi-argument, especially when highly linear within each variable When using partial least squares regression method is more effective. In addition, partial least squares regression better solution to the problem of the number of samples is less than the number of variables.)

自己做的一些Matlab GUI 简单的小程序，适合刚接触GUI的用户使用。(Matlab GUI to do some of their simple little program for users who are new to GUI.)

Butterworth滤波原代码程序，目标跟踪。(target tracking)

Feature display code for speech recognition.

解决局部寻优的单变量问题，在单变量无法用传统方法解决时，是一种很好的解决办法。代码包含详细解释。(Solving the local optimization problem of single variable is a good solution when single variable can not be solved by traditional methods. The code contains detailed explanations.)