project(i)

迭代多用户检测的MATLAB详细实现,非常全面,主要基于Vincent Poor和王晓东提出的算法实现,绝对的好东西.()

MATLAB程序设计之算法回归分析MATLAB programming algorithm for regression analysis of(MATLAB programming algorithm for regression analysis of MATLAB programming algorithm for regression analysis of)

使用小波变换提取信号的特征值，实现故障信号的视频分析，利用matlab进行仿真，证明判据的真实可信(wavelet based fault line detection)

SVM神经网络的回归预测分析---上证开盘指数预测,matlab的经典算法(SVM neural network regression analysis--- Shanghai opening index prediction, matlab classical algorithm)

将OFDM的过程讲解得很清楚，很适合开始学习OFDM及ＭＩＭＯＯＦＤＭ　的同志们参阅(OFDM process will explain very clearly, it is appropriate to begin to learn OFDM and comrades MIMOOFDM see)

3-D FDTD code of patch antenna in free space with Mur s boundary condition

Noise cancellation with Minimum Mean Square Error via LMS algorthim in Matlab simulation software

适合进行抗震分析计算。节省时间，可以计算出适合的反应谱、免费下载(Suitable for the calculation of seismic analysis. Save time, we can calculate the appropriate response spectrum,)

说明：  聚类的算法，包括K-means，密度聚类，密度比聚类，谱聚类等(Clustering algorithm, including k-means, density clustering, density ratio clustering, spectral clustering, etc)

蕞优分组周游路线问题是一类物流智能调度问题，是众多的计算难度极大的组合爆炸问题之一。自从运输问题最早是由Hitchcock在1941年提出的[2][4]后，人们对这一类问题给予了极大的关注并进行了大量的研究，提出了有效解决问题的优化算法——单纯形法的变形[2]。但由于问题规模的扩大，纯数学方法在允许的时间和空间上都很难找到问题的最优解，于是人们开始转向寻找问题的近似最优解，遗传算法以其在求解最优化问题中的独特的自组织性、自适应性很快进入人们的视野。 经典的遗传算法对于求解多目标的NP完全性问题非常有效，但对于有多个限制条件的多目标最优化问题缺显得有点力不从心，很难得到稳定度较高、收敛较快的解。本文提出的双层遗传算法模型给这一类问题提供了一个很好的解决方法，虽然建模条件限制了描述的问题的通用性，有待推广，但它能有效利用问题的领域知识，提高了求解效率，保证了解的可行性和最优性，算法思想极具推广潜力，可在其它物流智能调度问题求解算法设计中借鉴。(The most optimal packet travel route problem is a kind of intelligent logistic scheduling problem is one of combinatorial calculation difficult problem many. Since the transportation problem was first proposed by Hitchcock in 1941 after the [2][4], the people of this class of problems given great attention and made a lot of research, the deformation of [2] optimization, simplex method algorithm is effective to solve the problem of. But due to the expansion of the scale of the problem, the pure mathematical methods in the allowed time and space are very difficult to find the optimal solution of the problem, so people begin to turn to the approximate optimal solution for the problem, genetic algorithm in solving optimization problems with its unique in the self-organizing, adaptive soon entered people s field of vision. Very effective classical genetic algorithm for solving multi-objective NP complete problems, but for the multi-objective optimization problem with multiple constraints. I)