ARandARMA

time-invariant degradation models and stationary signals and noise, the classical Fourier domain Wiener filter, which can be implemented in O(N logN) time, gives the minimum mean-square-error estimate of the original undistorted signal. For time-varying degradations and nonstationary processes, however, the optimal linear estimate requires O(N2) time for implementation. We consider filtering in fractional Fourier domains, which enables significant reduction of the error compared with ordinary Fourier domain filtering for certain types of degradation and noise (especially of chirped nature), while requiring only O(N logN) implementation time. Thus, improved performance is achieved at no additional cost. Expressions for the optimal filter functions in fractional domains are derived, and several illustrative examples are given in which significant reduction of the error (by a factor of 50) is obtained.

仿真火车站的站场图，可以办理进路，显示信号机，单操，单锁道岔等功能(Simulation Station station map, you can handle into the road.)

鲁棒控制作业，包含详细的设计报告及matlab仿真代码(Robust control operations, including detailed design reports and matlab simulation code)

Matlab FieldII for windows.版本7.2(Windows that runs Matlab 7.10, 32 and 64 bits Field_II_PC7.zip )

高等应用数学问题的MATLAB求解，参考书(MATLAB solution of higher applied Mathematics problem, reference book)

SBL 稀疏贝叶斯学习方法,正弦信号验证版本,低噪声情况准确率高(sparse beyesian learning )

一级倒立摆仿真程序，实现了一级倒立摆的摆起稳定控制(one inverted pendulum simulation program, to achieve a level of stability control for inverted pendulum swing-up)

数模源程序，港口模拟源程序，正在学数模的可以下载，到时直接应用，或者借鉴。。。(Digital-analog source, port analog source, is learning digital-analog can be downloaded, when applied directly, or draw. . .)

这是一个matlab人脸识别程序代码，有GUI界面(This is a face recognition matlab code, there is a GUI interface)

说明：  径向基函数神经网络的优点：逼近能力，分类能力和学习速度等方面都优于BP神经网络，结构简单、训练简洁、学习收敛速度快、能够逼近任意非线性函数，克服局部极小值问题。原因在于其参数初始化具有一定的方法，并非随机初始化。代码为RBF的matlab程序，可以使用但是需要自己配置数据。(Radial Basis Function Neural Network (RBF-NN) has the advantages of better approximation ability, classification ability and learning speed than BP Neural Network (BP Neural Network). It has simple structure, simple training, fast learning convergence, and can approximate any non-linear function to overcome the local minimum problem. The reason is that its parameter initialization has a certain method, not random initialization.)