duochuanganqi

本程序执行CDMA仿真并确定半马尔克夫模型，输出为误比特率以及所需的游程。希望你能喜欢。(this program simulated the CDMA and get the HMM .the ouput are ber and run-length. i hope you can like it )

说明：  16psk格雷映射的误码率仿真,已通过matlab仿真，绝对好用( Bit Error Rate for 16-PSK modulation using Gray modulation mapping you can know 16psk modulation and gray modulation and apply them in your program)

个人项目，旅馆的查询设置例子。不要重复上码或其他好源可用下载数就会(Personal items, check the hotel setting an example. Do not repeat a good source code or other downloads will be available)

系统实时仿真开发环境与应用,主要讲解matlab RTW技术分上下两部分。(Real-time simulation development environment and application, mainly on matlab RTW technology was divided into two parts.)

一个很好的个人管理软件 已经测试过的 可以使用 无毒 放心下载(A good personal management software has been tested using non-toxic assured Download)

16-QAM constelation Source code for 16-QAM constelation for various signal to noise ratio levels in dB

matlab中关于结构动力计算的，小程序，采用纽马克方法计算的两个自由度的小程序。(matlab matlab newmark method，dynamic response of structres.)

K-SVD是一种经典的字典训练算法，依据误差最小原则，对误差项进行SVD分解，选择使误差最小的分解项作为更新的字典原子和对应的原子系数，经过不断的迭代从而得到优化的解。(K-SVD is a classic dictionary training algorithm, based on the principle of minimum error, the error items SVD decomposition, choose to make the smallest error decomposition item as an updated dictionary of atoms and atomic corresponding coefficient, through constant iteration to obtain an optimized solution.)

LLE算法基于swiss roll与s-curve数据集的运行结果，选取的样本点数为N=2000、k=12、 d=2进行的降维运算的结果,由运算结果可知在不同的数据集上LLE算法依然能够很好地保持数据在降维前后结构的特点。(The LLE algorithm is based on the running results of Swiss Roll and S-curve data sets, and the number of selected sample points is the result of dimensionality reduction of N=2000, k=12 and d=2. It is known that LLE algorithm still can maintain the characteristics of the data before and after dimensionality reduction well on different data sets.)

基于奇异值分解的核线性判别分析（Kernel Discriminant Analysis via QR Decomposition）。 (based on the singular value decomposition of the nuclear linear discriminant analysis (Kernel Discriminant An alysis via QR Decomposition).)