vi_CFBG

结合PCA的尺度不变特征变换(SIFT)算法源代码，可用于图像目标检测和识别。(combine PCA scale-invariant feature transformation (metabolism) algorithm source code, images can be used to target detection and identification.)

The toolbox contains functions to estimate and simulate multivariate copula GARCH models and Copula Vines. Supported copulas are the Gaussian and the T Copula. For the dynamic correlations, various specifications are supported.

仿真了雷达系统中信号产生、回波信号、脉冲压缩、MTI、MTD与恒虚警检测的过程。(Simulated radar signal generation system, the echo signal, pulse compression, the process of MTI, MTD and CFAR detection.)

雷达动目标检测，含MTD、MTI、CFAR处理(radar moving target detection)

Model of synchronious motor with PID regulators of speed and current

实际工程试验应用，从滞回曲线中提取骨架曲线(it will help solve some qusetions)

MATLAB写的CRC校验编码和解码程序(CRC check code written in MATLAB and the decoder)

this is a game which is very simple,

DC-DC升降压电路,主要实现升压和降压功能，采用PI电压电流双环控制(DC-DC buck-boost circuit, the main step-up and step-down function, using the PI voltage and current loop control)

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).)