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SF_CAPUTE
高动态扩频信号快速捕获方法的研究，提供了一种较好的实现信号捕获的方法(Rapid acquisition of high-dynamic spread-spectrum signals research on methods to provide a better way to achieve signal acquisition)
- 2010-03-10 20:12:50下载
- 积分：1
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DragonCurve11069
以matlab畫碎形中的dragon curve，非原創(Matlab fractal painting to the dragon curve, non-original)
- 2008-04-28 09:28:44下载
- 积分：1
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strapdown
自编的SINS仿真Matlab程序，对初学者还算全，应该有点用处，虽然比较简单，也省些前期编写的麻烦(The SINS self-compiled Matlab simulation program, for beginners fairly full, it should be somewhat useful, though relatively simple, but also save some trouble of pre-prepared)
- 2009-12-10 15:01:17下载
- 积分：1
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test
说明： MMATLAB求在直方图均衡化方面的应用程序详解示例(MMATLAB demand side in the histogram equalization example of the application Xiangjie)
- 2010-04-06 23:57:31下载
- 积分：1
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dtwica
一种利用小波包变换进行盲信号分离的程序。(Program for blind source separation using wavelet packet transform.)
- 2013-02-05 01:05:18下载
- 积分：1
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qianxiangpinghua
DOA,估计中的前向平滑运算的matlab程序，经过验证可以直接使用(DOA, estimated before the smooth operation matlab program can be used directly, proven)
- 2013-05-04 09:40:06下载
- 积分：1
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2
说明： solar pv mppt wind mppt using fuzzy logic control
- 2012-06-15 15:14:25下载
- 积分：1
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bwyang1
超宽带中的频谱检测算法的MATLAB实现，实用价值高。(Ultra-wideband spectrum detection algorithm in the MATLAB realization of high practical value.)
- 2009-01-08 11:44:13下载
- 积分：1
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Matlab_LMS3
自适应线性预测编码问题，利用白噪声序列生成信号序列，进而进行LMS迭代，计算滤波器权系数的轨迹曲线和衰减曲线(Adaptive linear predictive coding problem, the use of white noise sequence to generate signal sequence, and then LMS iteration to calculate the filter weights of the trajectory curve and the attenuation curve)
- 2008-04-25 10:22:33下载
- 积分：1
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work
matlab 关于association rule 的自己写的函数，有3个文件,
association.m：h = association(m, i, j)
i=>j, m是数据，h是support和confidence，该函数只适用于单个数据
ass_item: h=ass_itset(m, a, b)
同上，但是可用于多个数据（m为数组）
assrule: h = assrule(m, threshold1, threshold2)
该函数用于classification， 得到规则，threshold1为要求的support,threshold2为要求的confidence，h 则为符合要求的规则及其support和confidence，前2列为规则，后2列为其support和confidence
(matlab on the association rule to write functions, there are 3 files, association.m: h = association (m, i, j) i => j, m is the data, h is the support and confidence, this function applies only to a single Data
ass_item: h = ass_itset (m, a, b) it is the same as above, but it can be used for multiple data (m can be matrix)
assrule: h = assrule (m, threshold1, threshold2) the function used for classification,get the rules, threshold1 is the require of support, threshold2 is the required of confidence, h is the rules and their support and confidence, the former two columns as a rule, the latter two columns as one of its support and confidence)
- 2009-12-15 02:51:44下载
- 积分：1
