hpfilter

Matlab和VC混合编程，VC6.0工程设置，图文介绍很详细，在Matlab和VC混合编程中，这一步设置非常重要(Matlab and VC hybrid programming, VC6.0 engineering settings, graphic description is very detailed and hybrid programming in Matlab and VC, this step is quite important)

这是matlab的中文版，希望对大家有所帮助(This is the Chinese version of matlab, want to help everyone)

说明：  a file to genreate 16psk signal

层状压电圧磁结构中表面波的传播，表面波分SH表面波和瑞丽表面波(Layered magnetic structure of piezoelectric pressure wave propagation, wave and wave of SH wave Ruili)

bfsk调制解调误码率仿真，需要用MATLAB来实现的好东西(bfsk modem bit error rate simulation, need to use MATLAB to implement the good stuff)

雷达信号的基本模型，高斯分布，几何分布，求信号的概率分布，概率密度函数，功率谱函数，自相关函数等等(The basic model of the radar signal, Gaussian distribution, geometric distribution, the probability distribution for the signal, probability density function, power spectrum, the autocorrelation function, and so)

Sheffield工具箱的简单例子，有作者修改，其中doc文件为介绍(the example of Sheffield GA)

tsvd 截断奇异值分解 相比较奇异值分解，有很好的抗干扰能力(The singular value decomposition of TSVD truncated singular value decomposition has good anti-interference ability.)

QPSK误码率仿真程序，给出了比较可行，准确的方法(QPSK BER simulation program are given more feasible and accurate method)

拟牛顿法和最速下降法(Steepest Descent Methods)一样只要求每一步迭代时知道目标函数的梯度。通过测量梯度的变化，构造一个目标函数的模型使之足以产生超线性收敛性。这类方法大大优于最速下降法，尤其对于困难的问题。另外，因为拟牛顿法不需要二阶导数的信息，所以有时比牛顿法(Newton s Method)更为有效。如今，优化软件中包含了大量的拟牛顿算法用来解决无约束，约束，和大规模的优化问题。(The quasi-Newton method and the Steepest Descent Methods only require that each step iterations know the gradient of the objective function. By measuring the change of the gradient, constructing a model of the objective function is sufficient to produce superlinear convergence. This method is much better than the steepest descent method, especially for difficult problems. In addition, because the quasi-Newton method does not require information on the second derivative, it is sometimes more effective than the Newton s Method. Today, the optimization software contains a large number of quasi-Newton algorithm used to solve the unconstrained, constraint, and large-scale optimization problems.)