AlamoutiSchemewithGUI-PerformsMonte-Carlosimulatio

基于matlab的锁相环的仿真，注释详尽(Phase-locked loop based on matlab simulation, comments in detail)

本文利用软件相移技术得到相互正交的参考正弦波信号；通过互相关算法，完成了软正交矢量型LIA相关器的具体实现；利用该方法实现了对微弱信号幅值和相位的检测，有效地抑制干扰，减少了硬件电路成本。通过Matlab仿真，验证了该算法具有一定的优越性。(In this paper, the software technology is orthogonal to the phase shift of the reference sine wave signal through cross-correlation algorithm to complete the implementation of soft orthogonal vector LIA correlator achieved using this method for detecting a weak signal amplitude and phase, effectively suppress the interference and reduce the cost of the hardware circuit. By Matlab simulation show that the algorithm has certain advantages.)

说明：  该程序包里包括了很多跟小波变换有关的经典算法，希望学习小波的朋友可以在其中找到你们需要的代码(The package includes many, many related to the classical wavelet transform algorithm, wavelet friends want to learn can find the code you need)

IEEE 2013 base papers on power electronics

核主分量计算得也很有用的程序~已经调通很好用~(KPCA，which is very useful in signal processing!)

this program estimate cumulant order (8,0) for mqam modulation classification and plot them

说明：  用vb写的遗传算法程序。是一些通用代码，与具体问题对应的代码空出来，用的时候填进去。(used to write the genetic algorithm. Some common code, with the specific issues the corresponding code space, and the time spent filling into account.)

The M-files that are in the folders "Operations," "Extension Principle," and "Plotting" are all keyed into a specific chapter of the book Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, by Jerry M. Mendel, and published by Prentice-Hall, 2000. There are also some miscellaneous M-files.

MATLAB快速入门学习资料，讲解通俗易懂，易于初学者学习(Study on MATLAB)

BP神经网络容易陷于局部极小值，PSO算法在无约束非线性函数优化方面性能优越，通常可以直接找寻到全局最优解，即使不能搜多到全局最优解，也距离全局最优点不远。当然，基本PSO算法陷入局部极值也是有的。对于这个缺点目前还没有找到比较有效、省市的解决方案。本案例实现利用PSO算法和BP算法共同训练神经网络，先将网络进行PSO算法训练，然后BP算法接着进行小范围精细搜索，PSO算法训练神经网络的本质就是将输出误差函数（即能量函数）看成目标函数，PSO对能量函数进行全局寻找最小值。(BP neural networks are prone to local minimum values. The PS algorithm has superior performance in the optimization of unconstrained nonlinear functions. It can usually find the global optimal solution directly. Even if it can not find more global optimal solutions, it is not far from the global best. Of course, there is also a local extreme of the basic PO algorithm. For this shortcoming, there is no more effective, provincial and municipal solution. This case realizes the use of the SO algorithm and the BP algorithm to train the neural network. First, the network is trained in the SO algorithm, and then the BP algorithm is followed by a small range of fine searches. The essence of the PO algorithm training neural network is to regard the output error function(ie, the energy function) as the objective function, and the PO seeks a global minimum value for the energy function.)