EFG3D

Simulink sequence dependent setuptimes example

基于MATLAB对特定的图像进行旋转校正，用来处理各种所需要提取的场面。( 基于MATLAB对特定的图像进行旋转校正，用来处理各种所需要提取的场面。 基于MATLAB对特定的图像进行旋转校正，用来处理各种所需要提取的场面。 MATLAB based image rotation correction of specific, used to treat the need to extract the scene. 基于MATLAB对特定的图像进行旋转校正，用来处理各种所需要提取的场面。 基于MATLAB对特定的图像进行旋转校正，用来处理各种所需要提取的场面。 MATLAB based image rotation correction of specific, used to treat the need to extract the scene. )

PMSM矢量控制 包含了clark变换，clark逆变换，park变换，park逆变换等等。(PMSM vector control contains the Clark transform, Clark inverse transform, park transform, park inverse transform, and so on)

说明：  《自适应信号处理》书中各个章节的matlab仿真的源程序！对于学习自适应信号处理的非常有用！("adaptive signal processing" all the chapters of the book Matlab simulation of the source! Learning adaptive signal processing is very useful!)

Code for watermarking

TSP问题蚁群算法通用MATLAB源程序(TSP problem ant colony algorithm universal MATLAB source)

用matlab实现二次曲面图绘制，用来绘制曲线图(Draw a graph quadric)

GALGORITHM OPTIMZATION TECHNIQUE

利用matlab编写的扩频通信中涉及到的kasami小集序列，并验证其性质(Spread Spectrum Communication kasami sequence)

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).)