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compoversamp
说明： 此程序是抽样和计算自相关子程序，可以为我们提供参考(This procedure is sampling and calculation of autocorrelation subroutine, you can provide us with a reference)
- 2008-10-16 20:33:52下载
- 积分：1
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minimum-phase-wavelet
最小相位子波生成:可调整信号的采样率，时长，最大振幅等(generate minimum phase wavelet
)
- 2012-07-21 08:12:30下载
- 积分：1
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tpasymmetrical
simulink model of an innovative asymmmetrical sinusoidal pulse width modulation technique. try to verify the performance. it is for study purpose..
- 2011-09-22 23:22:08下载
- 积分：1
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Aravind
In an image edge is more important to interpolate the missing pixel values.
In similarity algorithm edge is detected using the unified high frequency map.
Based on this the missing green pixels are interpolated by taking the average of surrounding values. Then the missing red and blue values are interpolated similarly.
- 2014-01-28 12:17:16下载
- 积分：1
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SINS
严恭敏：基于matlab的捷联惯导算法设计与仿真(SINS algorithm design and simulation)
- 2015-04-16 14:37:15下载
- 积分：1
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mmlse_dynamic_coeffs_small_frame
simuink help for mlse from mathworks
- 2011-02-15 23:25:01下载
- 积分：1
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ber_vs_rx
MIMO系统误码率随接受天线数目变化而变化，MATLAB仿真程序，源代码(MIMO system bit error rate with the number of antennas to accept changes, MATLAB simulation program, the source code)
- 2013-04-22 19:24:50下载
- 积分：1
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Advanced-Engineering-Mathematics-With-Matlab
Advanced Engineering Mathematics With Matlab
- 2012-02-05 07:25:50下载
- 积分：1
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DeeBNetV2.2
深度置信网络源码，有配合的文档可以参考，详见内容(DBN source code)
- 2020-12-22 17:39:07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
