shiyan

温纳滤波，运用在影像上面的工具，简单的作业程式。(Wenner filter, using the tools in the image above, a simple job application.)

matlab cod for array processing music code

说明：  matlab 3d曲面回歸擬合教學範例 ptt檔(matlab 3d surface regression practices to ptt file)

这个是用matlab现实薇薇安尼曲线的源程序，非常的方便，解压后放入matlab中即可运行出结果。(This is matlab practical source program called vvanni， very convenient, after decompression can run results in matlab.)

The project for matrix mutltiplication in assembly x86 language.

一个著名的控制软件，现在会被查杀，但是可以作为参考学习(A well-known control software, will now be killing, but can serve as a reference for learning)

LLE是一种新的算法，既有非线性的特点，又有线性的优点(LLE is a new algorithm, both non-linear characteristics, but also the linear advantages)

灰色系统GM（1，1）模型的源代码，实现在数据不充分的情况下对未来的预测。(Gray system GM (1,1) model of the source code, data is not sufficient to achieve in the case of predictions for the future.)

虚拟力的无线传感网络覆盖，光纤无线通信系统中传输性能的研究，music高阶谱分析算法。( Virtual power wireless sensor network coverage, Fiber Transmiss)

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).)