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Opportunistic Routing

于 2019-01-06 发布

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下载积分: 1 下载次数: 4

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说明： Markovian_model_for_Opportunistic_Request_Routing-maste的matlabr例程，同时还有相关的论文。《Analyzing Opportunisitic Request Routing in Wireless Cache Networks》(A simulation of Markovian_model_for_Opportunistic_Request_Routing based on matlab)

文件列表：

Markovian_model_for_Opportunistic_Request_Routing-master, 0 , 2018-06-15
Markovian_model_for_Opportunistic_Request_Routing-master\.DS_Store, 6148 , 2018-06-15
Markovian_model_for_Opportunistic_Request_Routing-master\1_Rayleigh_Fading_Model, 0 , 2018-06-15
Markovian_model_for_Opportunistic_Request_Routing-master\1_Rayleigh_Fading_Model\.DS_Store, 6148 , 2018-06-15
Markovian_model_for_Opportunistic_Request_Routing-master\1_Rayleigh_Fading_Model\Throughput.m, 387 , 2018-06-15
Markovian_model_for_Opportunistic_Request_Routing-master\1_Rayleigh_Fading_Model\calcH.m, 205 , 2018-06-15
Markovian_model_for_Opportunistic_Request_Routing-master\1_Rayleigh_Fading_Model\getTransMatrix.m, 4134 , 2018-06-15
Markovian_model_for_Opportunistic_Request_Routing-master\1_Rayleigh_Fading_Model\gethopMatrix_1.m, 513 , 2018-06-15
Markovian_model_for_Opportunistic_Request_Routing-master\1_Rayleigh_Fading_Model\gethopMatrix_2.m, 640 , 2018-06-15
Markovian_model_for_Opportunistic_Request_Routing-master\1_Rayleigh_Fading_Model\hitMatrix.m, 594 , 2018-06-15
Markovian_model_for_Opportunistic_Request_Routing-master\1_Rayleigh_Fading_Model\main_numerical.m, 1153 , 2018-06-15
Markovian_model_for_Opportunistic_Request_Routing-master\1_Rayleigh_Fading_Model\prodV.m, 184 , 2018-06-15
Markovian_model_for_Opportunistic_Request_Routing-master\1_Rayleigh_Fading_Model\zipf_rand.m, 588 , 2018-06-15
Markovian_model_for_Opportunistic_Request_Routing-master\Readme.md, 508 , 2018-06-15

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chap06
这个是基于matlab的二次型的最优化问题的课件，值得一看。(This is based on the matlab quadratic optimization problem of the courseware, worth a visit.)

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互信息盲源分离，这基于这么一个事实，混合信号的互信息最小时，意味着信号独立。可以参考有关书籍。在google里面，搜索mutual information blind source separation.即可搜到文章。(mutual information Blind Source Separation, such a fact-based, mixed-signal information in the most hours of each other, mean signal independence. Can reference to the books. Google the inside, Search mutual information blind source separation . that article can be found.)

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tep 1：对遗传算法的运行参数进行赋值。参数包括种群规模、变量个数、交叉概率、变异概率以及遗传运算的终止进化代数。 Step 2：建立区域描述器。根据轨道交通与常规公交运营协调模型的求解变量的约束条件，设置变量的取值范围。 Step 3：在Step 2的变量取值范围内，随机产生初始群体，代入适应度函数计算其适应度值。 Step 4：执行比例选择算子进行选择操作。 Step 5：按交叉概率对交叉算子执行交叉操作。 Step 6：按变异概率执行离散变异操作。 Step 7：计算Step 6得到局部最优解中每个个体的适应值，并执行最优个体保存策略。 Step 8：判断是否满足遗传运算的终止进化代数，不满足则返回Step 4，满足则输出运算结果。其次，运用遗传算法工具箱。(Matlab genetic algorithm toolbox)

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complex network routing based on the lenght of queue of each node

2009-11-09 19:18:12下载

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详细介绍了Kalman滤波的理论知识，应用原理和应用环境(Kalman filter described in detail the theory of knowledge, theory and application environment application)

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