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work
for image processing
- 2009-02-09 15:19:35下载
- 积分:1
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MUSIC_DOA
DOA using MUSIC Method
- 2009-07-14 06:37:23下载
- 积分:1
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GRNN
基于BP和GRNN神经网络的粮食产量预测研究,通过训练样本和测试样本的交叉验证,实现粮食产量预测效果的最佳化(Prediction of Grain Yield BP and GRNN based training through cross-validation and testing samples, to achieve the best effect of the Grain Production Forecast)
- 2016-04-08 09:55:07下载
- 积分:1
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MyKmeans
实现聚类K均值算法: K均值算法:给定类的个数K,将n个对象分到K个类中去,使得类内对象之间的相似性最大,而类之间的相似性最小。 缺点:产生类的大小相差不会很大,对于脏数据很敏感。 改进的算法:k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用,这样的一个medoid就标识了这个类。步骤: 1,任意选取K个对象作为medoids(O1,O2,…Oi…Ok)。 以下是循环的: 2,将余下的对象分到各个类中去(根据与medoid最相近的原则); 3,对于每个类(Oi)中,顺序选取一个Or,计算用Or代替Oi后的消耗—E(Or)。选择E最小的那个Or来代替Oi。这样K个medoids就改变了,下面就再转到2。 4,这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感,但计算量显然要比K均值要大,一般只适合小数据量。(achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or))
- 2005-07-26 01:32:58下载
- 积分:1
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matrixqueue
*** Losided Iteration (A= P^T) ****
- 2012-04-27 09:48:21下载
- 积分:1
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EMD
希尔伯特-黄变换中利用EMD对一个信号进行分解,得到瞬态分量的程序(EMD for signal anaysis)
- 2014-12-05 13:49:07下载
- 积分:1
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cal
说明: 在原始信号加入白噪声,通过卡尔曼滤波去除噪声,恢复原始信号。(In the original signal to join the white noise through the kalman filter remove the noise, restore the original signal.
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- 2011-04-18 15:00:57下载
- 积分:1
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fftKernels
new kernel fast Fourier transform
- 2012-09-10 22:48:06下载
- 积分:1
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chap11
徑向基底函數類神經網路,是單隱藏層的3層前向網路,模擬人腦中局部調整,有很好的逼近能力(Radial basis function neural network, a single hidden layer of 3 layer to the network, simulating the brain partial adjust, has a very good approximation ability)
- 2007-10-29 18:25:26下载
- 积分:1
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CV
说明: test code for the previous code
- 2011-01-25 22:31:02下载
- 积分:1