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danjidaolibaizhinengkongzhiqisheji
单级倒立摆智能控制器的设计并仿真以及结果(Single inverted pendulum intelligent controller design and simulation and results)
- 2010-05-15 10:22:51下载
- 积分：1
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shanon
shannon channel codeing project
- 2015-01-17 11:20:17下载
- 积分：1
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about-atfm-and-test
使用MATLAB编程，关于四叉树的所有工具包及举例实验。(Using MATLAB programming, all kits on quadtree and example experiments.)
- 2015-03-18 17:26:17下载
- 积分：1
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hilbert-huang
hilbert-huang 分析代码，用于信号分析，振动(hilbert-huang analysis)
- 2015-11-01 15:12:09下载
- 积分：1
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SLM
选择映射法实现降低OFDM的峰值平均功率比的仿真(Mapping method chosen to achieve lower peak to average power ratio of OFDM-Simulation)
- 2010-05-17 16:50:29下载
- 积分：1
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1
说明： 人脸检测 123456789012323456780(face detec)
- 2011-10-20 19:26:32下载
- 积分：1
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symbolerrorratefor16QAM
Symbol error rate for 16QAM
- 2009-05-31 21:46:55下载
- 积分：1
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blocksandnoisdopp
1.对染噪doppler信号进行小波包3层分解：分解层次j=1,2时，都是信号的概貌；当j=3时，反映概貌的已几乎不含噪声分量，而其它噪声分量的幅值已很小。
2.对加噪Blocks信号进行不同阈值及不同阈值的使用方式降噪。
(1. On noise pollution doppler signal decomposition of wavelet packet Layer 3: decomposition level j = 1,2, are the signal profiles when j = 3 to reflect the general picture has been almost non-noise component, while other noise components amplitude has been very small. 2. Blocks of noise signals of different thresholds and different thresholds for the use of noise reduction methods.)
- 2008-06-24 10:01:41下载
- 积分：1
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prag1
包括MATLAB的简单函数命令功能。。。可以用来学习Matlab，都是些能实现一定功能的代码！M-file的格式。有50多个代码。(Including simple MATLAB function command function. . . Matlab, are more able to achieve a certain function code can be used to learn! M-file format. There are more than 50 code.)
- 2012-11-21 19:04:18下载
- 积分：1
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bilinear
In this paper, we introduce a new machine-learning-based data classification algorithm that is applied
to network intrusion detection. The basic task is to classify network activities (in the network log
as connection records) as normal or abnormal while minimizing misclassification. Although different
classification models have been developed for network intrusion detection, each of them has its strengths
and weaknesses, including the most commonly applied Support Vector Machine (SVM) method and the
Clustering based on Self-Organized Ant Colony Network (CSOACN). Our new approach combines the SVM
method with CSOACNs to take the advantages of both while avoiding their weaknesses. Our algorithm is
implemented and evaluated using a standard benchmark KDD99 data set. Experiments show that CSVAC
(Combining Support Vectors with Ant Colony) outperforms SVM alone or CSOACN alone in terms of both
classification rate and run-time efficiency.
- 2013-12-21 13:40:52下载
- 积分：1
