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CNM-algorithm

于 2019-04-22 发布

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下载积分: 1 下载次数: 5

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代码说明：

说明： CNM算法实现的快速社团检测，方便快捷好用(CNM Fast community detection based on the algorithm is convenient, fast and easy to use.)

文件列表：

CNM算法源代码\FastCommunity_GPL_v1.0.3\.DS_Store, 6148 , 2007-11-30
CNM算法源代码\FastCommunity_GPL_v1.0.3\FastCommunityMH, 75796 , 2008-04-07
CNM算法源代码\FastCommunity_GPL_v1.0.3\fastcommunity_mh.cc, 50925 , 2007-11-30
CNM算法源代码\FastCommunity_GPL_v1.0.3\gpl.txt, 18009 , 2007-04-23
CNM算法源代码\FastCommunity_GPL_v1.0.3\Makefile, 775 , 2007-04-23
CNM算法源代码\FastCommunity_GPL_v1.0.3\maxheap.h, 12689 , 2007-04-23
CNM算法源代码\FastCommunity_GPL_v1.0.3\vektor.h, 21537 , 2007-04-23
CNM算法源代码\FastCommunity_GPL_v1.0.3, 0 , 2008-04-07
CNM算法源代码, 0 , 2013-07-25

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2013-02-19 11:54:57下载

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The receiver performance is quantified by depicting the receiver operating char- acteristics (ROC) curves. This curves serve as a tool to and study the performance of a sensing scheme. ROC graphs are employed to show trade-of vs between detection probability and false alarm rates, (i.e. PD versus PFA), thus allowing the determination of an optimal threshold. To illustrate the performance of energy detectors, we generate complementary ROC curves (the average probability of miss detection $ P_{md} 1 - P_d$ versus the false alarm probability $P_f$ for different receiver structures and in different fading environments.Fig. 1 illustrates the complementary ROC curves for a single channel reception (no diversity) over AWGN channels. This figure depicts the complementary ROC curves for energy detectors.

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清华大学的中文版的Matlab基础教程，适合初学者入门用。喜欢的下(Tsinghua University, the Chinese version of the Matlab Basic Course, suitable for beginners to use portal. Like the next)

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模糊自整定PID控制器设计。本资料含有详细的PID控制器算法M文件、Simulink模型搭建、模糊控制器设计以及相应的技术文档分析。相信对您一定很有帮助(Fuzzy self-tuning PID controller design. This information contains details of the PID controller algorithm M files, Simulink models to build, fuzzy controller design and analysis of relevant technical documentation. I believe you must be helpful)

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IPM-OPF
程序用于求解电力系统最优潮流，采用原-对偶内点算法，并辅以文献，有利于对内点法的学习。(Procedures for solving optimal power flow, the use of primal- dual interior point algorithm, supplemented by literature, internal point of law in favor of learning.)

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