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orl_faces
人脸识别的一个图片库40人每个10张图像(Recognition of a picture gallery of 40 images each of 10)
- 2010-06-28 20:26:52下载
- 积分：1
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A-Guide-to-MATLAB-Object-Oriented-Programming
A Guide to MATLAB Object-Oriented Programming
- 2010-08-28 00:31:34下载
- 积分：1
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LDA
在大数据处理时常用的一种线性鉴别方法，里面有讲解和源代码程序(When large data processing methods commonly used linear discriminant, there are explanations and source code)
- 2013-12-11 15:46:24下载
- 积分：1
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speech-enhancement
语音增强算法，性噪比，原信号与增强后信号对比(Speech enhancement algorithm, sex ratio, the original signals and enhanced contrast
)
- 2015-03-03 16:54:10下载
- 积分：1
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FIR_pinlv
FIR滤波器， 设计一个线性相位有限冲激响应低通滤波器，使其满足如下指标：通带边界为2kHz，阻带边界为2.5kHz，通带波纹 =0.005，阻带波纹 =0.005，抽样率为10kHz。采用多尔夫-切比雪夫窗进行设计，进行频谱分析。(FIR filter, design a linear phase FIR low-pass filter to meet the following indicators: passband border to 2kHz, stopband border to 2.5kHz, passband ripple = 0.005, stop-band ripple = 0.005, sampling rate of 10kHz. Used Dorf- Chebyshev window design, spectral analysis.)
- 2012-11-19 22:49:33下载
- 积分：1
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CARIMA-model
CARIMA模型的程序，包括GPC的算法。(CARMA model procedures, including GPA algorithm.)
- 2020-11-19 21:19:37下载
- 积分：1
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PMSM_SMO_dq
说明： 离散型永磁同步电机滑模控制，采用锁相环实现角度估算，角度误差低，效果好。(PMSM_SMO_PLL,test is good.)
- 2021-02-21 12:59:42下载
- 积分：1
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grayrelated
灰色预测 GM（1,1） 灰色关联 matlab 数学建模(graysystem matlab)
- 2010-08-16 09:37:21下载
- 积分：1
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gmdh
GMDH Neural Network Implementation
- 2014-02-10 02:03:27下载
- 积分：1
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K-meanCluster
How the K-mean Cluster work
Step 1. Begin with a decision the value of k = number of clusters
Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following:
Take the first k training sample as single-element clusters
Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster.
Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample.
Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)
- 2007-11-15 01:49:03下载
- 积分：1
