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FDTDprogramme
二维时域有限差分法程序，用于电磁场环境下的功能仿真。(Two-dimensional FDTD program，Electromagnetic environment for functional simulation)
- 2009-03-23 19:25:27下载
- 积分：1
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emd
法国人的emd算法，2007年的版本，镜像延拓，效果已经很不错了(French emd algorithm, the 2007 version, mirror extension, the effect is quite satisfactory)
- 2010-07-26 10:57:33下载
- 积分：1
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PFCDCM0hm5
this is an pfc demo file1.
- 2012-05-14 11:19:43下载
- 积分：1
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exam2_1995
Elnagar, G., et al. (1995). "The pseudospectral Legendre method for discretizing optimal control problems." IEEE Transactions on Automatic Control 40(10).
上述文章的算例2的仿真代码(Elnagar, G., et al. (1995). " The pseudospectral Legendre method for discretizing optimal control problems." IEEE Transactions on Automatic Control 40 (10).
the simulation code of example 2 in the preceding article.)
- 2013-10-18 12:54:32下载
- 积分：1
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Newtint
牛顿插值多项式，拉格朗日插值多项式，误差分析(Newton interpolation polynomial, Lagrange interpolation polynomial, the error analysis
)
- 2013-11-18 18:10:52下载
- 积分：1
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CODES
I have submitted 5 matlab codes which are self made related to PAPR reduction, gabor filter, histogram, loading etc
- 2015-02-18 17:57:24下载
- 积分：1
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espec
Spectrogram implementation in matlab environment
- 2009-06-09 21:08:59下载
- 积分：1
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hmm_plusdemo
HMM在Matlab下的程序，包括源文件和Demo以及Hmm相似度的计算程序(HMM in Matlab, the following procedure including the source documents and Demo Hmm similarity and the computational procedures)
- 2007-04-06 15:22:47下载
- 积分：1
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LBM-two-phase
格子BLOTZMANN方法，两相流 matlab程序(Lattice BLOTZMANN method, two phase flow matlab program)
- 2021-03-12 16:49:24下载
- 积分：1
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NSGA
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).)
- 2020-02-13 19:30:43下载
- 积分：1
