simple_dwt

dct ofdm reduces ber so widely used

说明：  按照书本上教材编写的自适应中值滤波函数，很有参考价值(Materials prepared in accordance with the books adaptive median filtering function, a good reference)

基于NCD优化的非线性优化PID控制 （非线性系统的PID鲁棒控制）(NCD-based optimization of nonlinear optimization PID control (PID Robust Control of Nonlinear Systems))

给出一个逆变器具体的仿真实例，通过仿真 ，可以得出实际运行中的电压、电流的波形 (Give a concrete example of the simulation, the simulation can be drawn from the actual operation of the voltage and current waveforms)

程序主要包含幂级数展开逼近余弦及在同一坐标下显示多个曲线。压缩包中还包含相关的参考资料。(Program mainly includes power series expansion approximation cosine and displayed in the same coordinate multiple curves. Compressed package also contains the associated reference materials.)

Approximate subspace-based iterative adaptive approach for fast two-dimensional spectral estimation

这是天津大学matlab讲义-赵国瑞 感觉讲得挺不错！(This is the Tianjin University matlab lecture- Zhao Guorui speak quite in a good feeling!)

计盒分形维数计算的matlab源代码编程实现过程(Function [Fdgen NoisfreFD ]=fractaldim(y,Q,M) __________________________________________________________________________ Usage: Computes fractal dimension by box counting(BC) method. NOTE: Running the code may take a little time, because it calculates dimensions for all embedding dimensions up to M. This code is based on an algorithm that constructs a box for the first observation and for other observations test which it belongs to previous box(s). If the observation belongs to one of the existent boxes then increases number of points in the relevant box,otherwise makes a new box. the algorithm of this code uses only rounding down and it does not need binary coding, sorting and so on. It uses only transformation data to [0,2^32-1] and finding valid boxes based on Leibovich and Toth(1989). Inputs: y is a vector time series. q stands for generalized dimension q=1 entropy dimension,q=2 correlation dimension and so on. q=0 is box counting dimen)

matlab下的两步法标定程序，应该对大家有所帮助(matlab under the two-step calibration process should be helpful to everyone)

Variational inference for Bayesian variable selection