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sept5
LMS算法源代码，输入为正弦加噪声LMS算法源代码，输入为正弦加噪声(LMS algorithm source code, the input is sinusoidal plus noise LMS algorithm source code, the input is sinusoidal plus noise)
- 2010-06-03 14:51:46下载
- 积分：1
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yichuansuanfa
包括遗传算法的原理介绍，及如何用matlab实现(genetic algorithm，matlab)
- 2012-04-04 17:01:02下载
- 积分：1
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PSK-Simulation
Phase shift keying simulation
- 2012-05-14 23:27:12下载
- 积分：1
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topsis3-1
this program is implementation of TOPSIS algorithm
- 2013-09-23 11:17:25下载
- 积分：1
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naveen_ti_sift
SIFT DESCRIPTOR SOURCE CODE
- 2014-09-24 13:35:21下载
- 积分：1
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简单的文本框数据传递
MATLAB GUI 简单的文本框数据传递(Simple Text Box Data Transfer in MATLAB GUI)
- 2020-06-19 01:40:01下载
- 积分：1
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morphology-reconstruction
形态学重构-主要是为了复杂背景条件下的图像分割，采用matlab语言编写，具有很高的参考价值。(Morphological Reconstruction- mainly to under complex background image segmentation, using matlab language, with a high reference value.)
- 2009-12-15 23:12:15下载
- 积分：1
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Matlab
两本matlab方面的英文原版书，很难得的，欢迎大家下载(Matlab two aspects of the original English edition book, a rare and welcome you to download)
- 2008-12-17 18:28:24下载
- 积分：1
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ruilixindaoguji
matlab环境下对时变，多径信道进行估计，服从瑞利分布。。。(Prediction program of wireless channel in Matlab environment)
- 2015-03-27 11:29:26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
