Differential-space-time-coding

Communication modeling for Gold code

文献1的仿真程序 solves the linear least squares problem with nonnegative variables = using the newton s algorithm in [1]. [1] Portugal, Judice and Vicente, A comparison of block pivoting and ％ interior point algorithms for linear least squares problems with ％ nonnegative variables, Mathematics of Computation, 63(1994), pp. = 625-643(literature of a simulation program solves the linear least squares p roblem with nonnegative variables using the n = ewton's algorithm in [1]. [1] Portugal. Judice and Vicente. A comparison of block pivoting and interior% po int algorithms for linear least squares proble ms% with nonnegative variables, Mathematics of Computation, 63 (1994), pp. = 625-643)

灰色预测模型,灰色预测模型,灰色预测模型,灰色预测模型,灰色预测模型,灰色预测模型,灰色预测模型,灰色预测模型,(Gray prediction model, gray forecasting model, gray forecasting model, gray forecasting model, gray forecasting model, gray forecasting model, gray forecasting model, gray forecasting model,)

Matlab implementation for classic kalman filter predicting linear motion.

说明：  Permanence脉冲微分方程程序用matlab模拟种群的灭绝和持续生存(Permanence Impulsive Differential Equation Program Using matlab to Simulate the Extinction and Persistence of Population)

说明：  这个是交通流ＣＡ模型，能够较为真实的仿真出交通流的运行状况，对交通流元胞自动机的人可以学习学习(The CA model of traffic flow, to a more realistic simulation of the operational status of the traffic flow, traffic flow cellular automaton can learn to learn)

该文档为脱离MATLAB环境，安装MCR后仍然不能运行程序的解决方法。(The document is from the MATLAB environment, the installation still can not run the program after the MCR solution.)

用 matlab实现均值滤波去高斯白噪声 用 matlab实现中值滤波去噪声 用理想低通滤波器在频率域实现低通滤波(Mean filter with matlab to implement Gaussian white noise with median filter matlab to achieve the ideal low-pass filter with the noise in the frequency domain low pass filter)

slidingmode control for wind energy

说明：  使用Fisher 现行判别函数对给定的样本进行训练，对于两类，Fisher线性判别函数很好做，就是 w = Sw (m1 - m2), 其中：Sw为总类内离散度矩阵， m1, m2, 为两个模式类的均值(Fisher discriminant function using the current sample of a given training for two, Fisher linear discriminant function is well done, that is, w = Sw ' (m1- m2), where: Sw of the total within-class scatter matrices, m1, m2, the mean value for the two pattern class)