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D-FNN
基于matlab实现的动态模糊网络，值得收藏借鉴！(Based on the matlab implementation of dynamic fuzzy network, worth collecting reference!)
- 2011-09-26 15:11:05下载
- 积分：1
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faceprotecte
face protecte program
- 2010-05-21 04:14:52下载
- 积分：1
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jeibai_v85
时间序列数据分析中的梅林变换工具，数据包传送源码程序，wolf 方法计算李雅普诺夫指数。( Time series data analysis Mellin transform tool, Data packet transfer source program, wolf calculated Lyapunov exponent.)
- 2017-01-12 12:54:21下载
- 积分：1
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cthd39-40
电力系统40节点潮流计算程序 已用过调试(calculation of power flow)
- 2010-12-27 21:34:46下载
- 积分：1
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ssb
说明： 基于MATLAB的SSB调制课程设计论文与源程序(MATLAB-based curriculum design of the SSB modulation papers and source code)
- 2008-10-22 17:26:18下载
- 积分：1
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states_densityofPHC
计算光子晶体态密度的程序，与文献吻合，只要小的修改就可计算其他结构的态密度(photonic crystal calculation of the density of states procedures, in line with the literature, as long as minor modifications other structures can be calculated density of states)
- 2021-04-27 09:08:45下载
- 积分：1
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fixtime_control
单交叉口的定时控制的matlab源代码，毕业设计的东西，搞得好辛苦啊(Single intersection timing control matlab source code graduation design stuff, good job hard ah)
- 2014-04-03 11:01:15下载
- 积分：1
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峰值信噪比的函数psnr matlab
在matlab中实现峰值信噪比的函数 function y=psnr(im1,im2) 计算峰值信噪比程序——————————————— - ininput im1 : the original image matrix (For peak signal to noise ratio in matlab function function y = psnr (im1, im2) calculated PSNR program)
- 2016-06-15 15:49:39下载
- 积分：1
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dream_Win64_v2.1.3
dream_Win64_v2.1.3工具箱，功能强大，能够计算不同形状超声传感器的声场(the dream_Win64_v2.1.3 toolbox, powerful, able to calculate the different shapes ultrasonic transducer sound field)
- 2013-05-10 09:09:47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
