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adaptive
自适应代码，根据论文写得，结果和论文一样，可供参考。(Adaptive code, according to papers written, and papers as a result, for reference.)
- 2013-12-23 22:20:30下载
- 积分：1
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transformation
世界坐标系转换
二维和三维的坐标系compound(The world coordinate system transformation)
- 2017-01-20 22:06:17下载
- 积分：1
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matlab
matlab中有语音处理所需的分帧函数和重叠相加函数，调用起来很简单。(matlab in the sub-frame required for voice processing functions and overlap-add function, call it so easy.)
- 2010-12-26 23:58:58下载
- 积分：1
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OFDM_FOR_WIRELESS_COMM_SYS
An E-BOOK on OFDM basics and PAPR reduction Techniques
- 2011-01-03 17:30:25下载
- 积分：1
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用Matlab语言实现线性方程组的全主元三角分解法
说明： 用Matlab语言实现线性方程组的全主元三角分解法.PDF(using Matlab linear equations of all the main yuan triangular decomposition. PDF)
- 2005-12-10 20:29:14下载
- 积分：1
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Chuong-trinh-mau-bai-tap-9-_-K2012
Exercise about modelling and identification system
- 2013-05-01 21:37:58下载
- 积分：1
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textDetector
a source code in matlab that detect text contour in images
- 2015-04-06 01:46:57下载
- 积分：1
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Quatre_QAM_AWGN
modulation ofdm qutre qam sur canal awgn
- 2012-01-09 04:51:59下载
- 积分：1
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@linear
针对SVM法线特征筛选算法仅考虑法线对特征筛选的贡献,而忽略了特征分布对特征筛选的贡献的不足,在对SVM法线算法进行分析的基础上,基于特征在正、负例中出现概率的不同提出了加权SVM法线算法,该算法考虑到了法线和特征的分布.通过试验可以看出,在使用较小的特征空间时,与SVM法线算法和信息增益算法相比,加权SVM法线算法具有更好的特征筛选性能.(Normal feature selection for SVM algorithm only considered normal for the contribution of feature selection, to the neglect of the characteristics of the distribution of feature selection have contributed to the lack of normal SVM algorithm based on the analysis, based on the characteristics of the positive and negative cases emergence of a different probability-weighted normal SVM algorithm, which takes into account the distribution and characteristics of normal. through the test can be seen in the use of smaller feature space, the normal and the SVM algorithm and information gain algorithm, normal weighted SVM algorithm has better performance of feature selection.)
- 2008-01-08 21:38:17下载
- 积分：1
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1985528BP_RBF
ADIAL Basis Function (RBF) networks were introduced
into the neural network literature by Broomhead and
Lowe [1], which are motivated by observation on the local
response in biologic neurons. Due to their better
approximation capabilities, simpler network structures and
faster learning algorithms, RBF networks have been widely applied in many science and engineering fields. RBF network is three layers feedback network, where each hidden unit implements a radial activation function and each output unit implements a weighted sum of hidden units’ outputs.
- 2009-05-09 02:04:39下载
- 积分：1
