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qam_cma
通信系统的4qam盲均衡程序,希望对大家有点帮助(Communication systems 4qam equalization program, we hope to be helpful)
- 2010-10-14 23:38:42下载
- 积分:1
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originalbee
说明: 原始的蜂群演算法,提供圖形介面,提供初階使用者認識蜂群演算法(orginal antificial bee colony(ABC))
- 2010-03-17 11:02:15下载
- 积分:1
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bp_drag_new
对fluent默认的Syamlal drag law进行曳力修正,多相流模型中(drag modify model in fluent)
- 2013-12-14 11:52:06下载
- 积分:1
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K_distribution
用matlab仿真k分布雷达杂波,并进行处理(K-distribution radar clutter processing)
- 2016-04-24 11:12:31下载
- 积分:1
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PID_june
PID control for 2-dim Systems
- 2010-11-12 20:51:55下载
- 积分:1
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BaHF
本代码用于BaHF分子动力学的模拟计算,其中文件包里面的byt.inp为数据输入文件,不可丢掉哦(BaHF the code for the molecular dynamics simulation, which package inside byt.inp for data entry documents, not lose!)
- 2006-12-04 11:27:58下载
- 积分:1
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bbv
论文调研了复杂网络的结构特征,基于复杂网络的方法对在线社会网络的多种属性进行了分析,并利用MATLAB仿真工具对在线社会网络数据源进行了仿真,论文重点分析了在线社会网络的度分布、集聚系数、平均最短路径三个基本特征量,仿真实验结果表明在线社会网络具有小世界特性和无标度特性;然后,基于节点收缩的方法对网络节点重要性进行评估,根据节点重要度找出在线社会网络中的核心节点;进而,采用标签传播算法对在线社会网络进行社团划分,改进了算法的结条件,可在不影响划分结果的前提下,大大提高了运算效率。(Studies show that many social networks have the characteristics of the complex network, researching the basic characteristics of the network can help us to understand the various characteristics of social network By researching the importance of network nodes, we can find the Hub node of the network, it provides the basis of preventing the malicious attacks, protecting critical node or getting a better network transmission By researching the community structure of network can help us understand the relationship between the individual networks and achieve the maximization of network availability.)
- 2015-03-18 23:15:07下载
- 积分:1
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danxiangpwmnibian
建立了单相H桥逆变器单极性PWM调制仿真模型,运行效果良好(A unipolar PWM modulation simulation model of single-phase H bridge inverter is established, and the running effect is good)
- 2020-12-08 19:59:21下载
- 积分:1
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Basearraysignalprocessing
基阵信号处理的图形设计有平面设计,球面设计以及线列设计(Base array signal processing)
- 2010-05-10 16:19:27下载
- 积分:1
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SOM_NN_CODE
An important aspect of an ANN model is whether it needs
guidance in learning or not. Based on the way they learn, all
artificial neural networks can be divided into two learning
categories - supervised and unsupervised.
• In supervised learning, a desired output result for each input
vector is required when the network is trained. An ANN of the
supervised learning type, such as the multi-layer perceptron, uses
the target result to guide the formation of the neural parameters. It
is thus possible to make the neural network learn the behavior of
the process under study.
• In unsupervised learning, the training of the network is entirely
data-driven and no target results for the input data vectors are
provided. An ANN of the unsupervised learning type, such as the
self-organizing map, can be used for clustering the input data and
find features inherent to the problem.
- 2015-04-15 00:03:32下载
- 积分:1