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circuit_simulink
说明： 电路与系统专业的matlab程序。有很多经典的实例。(Circuits and Systems matlab professional procedures. There are many classic examples.)
- 2008-12-01 21:27:03下载
- 积分：1
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the-non-uniform-uniform-quantizer
用MATLAB实现13段折线A律压缩与均匀量化构成的非均匀量化器(13 line in MATLAB A-law compression and quantitative composition of the non-uniform uniform quantizer)
- 2011-06-30 12:06:56下载
- 积分：1
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duffing-construction
非线性动力学中吸引子相位图重构方法，很好的解决了混沌问题的关键问题(duffing construction)
- 2014-10-30 09:54:48下载
- 积分：1
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regression-analysis-for-6-factor
本代码可以实现对于6个数据的回归分析及处理，并可修改进行相关推广(This code can return to the six data analysis and processing, and modify the relevant promotion)
- 2012-09-08 08:59:44下载
- 积分：1
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fam
FFT accumulation method (FAM) in Matlab
- 2010-02-23 12:57:36下载
- 积分：1
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regiongrow
自适应区域生长算法MATLAB实现。可以较好完成单种子点的区域生长工作(Adaptive region growing algorithm MATLAB. Can better complete a single seed point region growing work)
- 2013-12-13 16:30:55下载
- 积分：1
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实验二
运用MATLAB对控制系统进行仿真操作(to use MATLAB simulation and control systems operation)
- 2004-11-28 16:16:41下载
- 积分：1
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Ten_Commonly_Algorithm_for_MATLAB
超过10种MATLAB常用算法：Floyd算法，动态规划,分治算法,概率算法,模拟退火算法,神经网络,搜索算法,贪婪算法,遗传算法,组合算法,等等(Over 10 types of commonly used MATLAB algorithms: Floyd algorithm, dynamic programming, divide and conquer algorithms, probabilistic algorithms, simulated annealing, neural networks, search algorithms, greedy algorithm, genetic algorithm, combinatorial algorithms, etc.)
- 2021-03-20 16:59:19下载
- 积分：1
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fractal
该程序可以实现信号的分形计算，从而求解出所求信号的分形函数，在信号非线性研究中有很重要的作用。(The program can calculate the fractal signal, thus solving the request signals a fractal function, the study of nonlinear signal has a very important role.)
- 2011-09-07 17:11:47下载
- 积分：1
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fs_sup_relieff
Relief算法中特征和类别的相关性是基于特征对近距离样本的区分能力。算法从训练集D中选择一个样本R，然后从和R同类的样本中寻找最近邻样本H，称为Near Hit，从和R不同类的样本中寻找最近样本M，称为Near Miss，根据以下规则更新每个特征的权重：
如果R和Near Hit在某个特征上的距离小于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻是有益的，则增加该特征的权重；反之，如果R和Near Hit在某个特征上的距离大于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻起负面作用，则降低该特征的权重。(The correlation between feature and category in Relief algorithm is based on distinguishing ability of feature to close sample. The algorithm selects a sample R from the training set D, and then searches for the nearest neighbor sample H from the samples of the same R, called Near Hit, and searches for the nearest sample M from the sample of the R dissimilar, called the Near Miss, and updates the weight of each feature according to the following rules:
If the distance between R and Near Hit on a certain feature is less than the distance between R and Near Miss, it shows that the feature is beneficial to the nearest neighbor of the same kind and dissimilar, and increases the weight of the feature; conversely, if the distance between R and Near Hit is greater than the distance on R and Near Miss, the feature is the same. The negative effect of nearest neighbor between class and different kind reduces the weight of the feature.)
- 2018-04-17 14:41:55下载
- 积分：1
