MatlabB-spline

关于数字信号处理的几篇PPT文档(IIR/FIR滤波器设计等)，适合数字信号处理入门学习(Digital signal processing on several PPT files (IIR/FIR filter design, etc.), suitable for digital signal processing Introduction to learning)

说明：  基于Matlab编写的用于对数据进行插值分析和傅立叶变换的程序(Based on Matlab for the preparation of the data analysis and Fourier transform interpolation procedures)

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.)

bpsk调制的完全调制解调过程仿真包括误码率，信号功率谱(bpsk complete modem modulation process)

the famous 3 machines 9 bus power systems on simpowersystems

shadow detection paper from net,it is value

采用一个改进的ip-iq的谐波检测算法，无PLL，能运行。(An improved ip-iq algorithm for harmonic detection is implemented without PLL)

此程序是通过MATLAB对ofdm基本原理的simulnk仿真程序，包括发送和接收端，能帮助学习ofdm的人更好的理解原理(This procedure are adopted by MATLAB on the basic principles of OFDM simulnk simulation program, including sending and receiving end, to help study OFDM people a better understanding of the principles of)

S4VM 对传统的S3VM 进行了改进。传统的S3VM 基于低密度假设，它试图找到一个低密度的分界线， 也就是更倾向于决策边界穿过特征空间的低密度区域。S4VM 和S3VM 的不同点在于，S3VM 试图把注意力 集中在一个最优的低密度分界线上，而S4VM 则同时关注多个可能的低密度分界线。之所以这样做，是因为 给定一些有标记的点和大量未标记的点，可能存在着不止一个“间隔”较大的低密度分界线（如图2 ），基于 有限的标记样本，很难决定哪个是最好的。虽然这些低密度分界线都与有限的标记样本吻合，但它们之间差 异很大，因此如果选择错了，会有一个很大的损失，导致性能下降。所以S4VM 试图考虑所有可能的低密度 分界线。 (Over the past decade, many improved versions are proposed to solve every different specific problem of semi-supervised support vector machines, for example: to the inefficiency issue, meanS3VM is introduced to performance degenerate problem, S4VM is proposed)

KSVD的工具包，版本为13；用于求解图像集字典的字典，以及去噪等(KSVDBox v13 October 18, 2009 Make sure OMPBox v10 is installed prior to installing this package. )