HHT

用四阶龙格库特法在matlab环境下实现某函数的适应度值的计算。(A fitness function value is calculated using fourth-order Runge Ge Kute method in Matlab environment.)

MIMO雷达下利用PASTd算法进行二维DOA跟踪，通过esprit估计每时刻二维角度(use PASTd to track DOA)

说明：  matlab读取iges文件程序，可导入特征数量、具体位置等多种信息。直接使用无需编辑。(Matlab read IGES file program, can import the number of features, specific location and other information. It can be used directly without editing.)

说明：  遗传算法用于求函数最大值，可以修改函数名和迭代次数(A function of genetic algorithm is used to seek the maximum, you can modify the function name and the number of iterations)

基于加权主成分分析（PCA）的人体检测算法，matlab程序(weighted PCA for pedestrian detection)

说明：  req dac两种格式数据文件转化，并产生最大最小值表格(convert .req to dac file)

解模块线性方程组, solve modular linear equations, 用到了extended-euclid算法求最大公约数...(solve modular linear equations, use extended-euclid to compute the greatest common divisor..)

local mean decomposition 软件原理描述(local mean decomposition Software Principles)

基于最小均方误差估计(mmse)的语音增强算法的研究的相关资料，期刊，论文(Based on the minimum mean square error estimation (mmse) speech enhancement algorithm relevant information, journals, papers)

说明：  多目标遗传算法是NSGA-II[1]（改进的非支配排序算法）,该遗传算法相比于其它的多目标遗传算法有如下优点：传统的非支配排序算法的复杂度为 ，而NSGA-II的复杂度为 ，其中M为目标函数的个数，N为种群中的个体数。引进精英策略，保证某些优良的种群个体在进化过程中不会被丢弃，从而提高了优化结果的精度。采用拥挤度和拥挤度比较算子，不但克服了NSGA中需要人为指定共享参数的缺陷，而且将其作为种群中个体间的比较标准，使得准Pareto域中的个体能均匀地扩展到整个Pareto域，保证了种群的多样性。（消除了共享参数）。(Multi-objective genetic algorithm is nsga-ii [1] (improved non-dominant sorting algorithm), which has the following advantages compared with other multi-objective genetic algorithms: the complexity of the traditional non-dominant sorting algorithm is, while the complexity of nsga-ii is, where M is the number of objective functions and N is the number of individuals in the population.The introduction of elite strategy to ensure that some good individuals in the evolutionary process will not be discarded, thus improving the accuracy of the optimization results.The comparison operator of crowding degree and crowding degree not only overcomes the defect that NSGA needs to specify the Shared parameter artificially, but also takes it as the comparison standard between individuals in the population, so that individuals in the quasi-pareto domain can uniformly expand to the whole Pareto domain, ensuring the diversity of the population.(eliminating Shared parameters).)