360compkill5.0

用matlab求解有限元计算，对使用有限元方法计算的内部过程有更深的了解(Using matlab to solve finite element, finite element method for the use of a deeper understanding of internal process)

This is matlab simulation of selective mapping technique for papr reduction.Sequences that are independent and represent the same information are generated.PAPR of these are calculated separately. Sequence with the smallest PAPR is selected for final serial transmission

该程序包包括了matlab 优化工具箱里函数的介绍及具体实例。有一个配套使用的视频教程，在优酷里搜索“matlab 优化工具箱入门经典教程”。希望对学习优化算法的有用。 (The package includes the matlab optimization toolbox function description and specific examples. Supporting the use of a video tutorial, the excellent Cool Search " matlab optimization toolbox tutorial Introduction to the classic." Hope to learn a useful optimization.)

matlab中信号处理的源程序，机械专业进行转子动力学分析时用(source code matlab signal processing, machinery specialized for rotor dynamics analysis using)

说明：  Simulation Montecarlo Integral

基于MATLAB的QPSK通信系统仿真源码，信道误码率等(MATLAB-based simulation of communication systems QPSK source, channel bit error rate)

画电偶极子等势面，电偶极子的三维等势面，可以改变x，y值(Painting equipotential surface dipole)

BPSK QPSK 16QAM BFSK 在高斯信道下，瑞丽信道下，多径（三条径）下的误码率仿真对比，(BPSK QPSK 16QAM BFSK in Gaussian channel, under Ruili Channel, multipath error rate (three diameter) under the simulation comparison,)

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)

小电流接地故障选线文献，包含零序电流作为特征分量，神经网络故障诊断方法等(Small current grounding fault line selection literature, contain zero-sequence current as a characteristic component of the neural network fault diagnosis method)