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faogen_v44
小波包分析提取振动信号中的特征频率，D-S证据理论数据融合，高斯白噪声的生成程序。( Wavelet packet analysis to extract vibration signal characteristic frequency, D-S evidence theory data fusion, Gaussian white noise generator.)
- 2017-01-06 10:42:05下载
- 积分：1
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NeuralNetwork_BP_Classification
BP neural netowrk regression
- 2012-10-02 02:57:21下载
- 积分：1
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elm-kernel
The MATLAB codes ELM with kernels (for both regression and multi-class classification) work linearly similarly to ELM with random hidden nodes. For the sake of convenience, the source codes of ELM with kernels are given separately.
- 2020-12-04 15:59:23下载
- 积分：1
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finggui_v36
小波包分析提取振动信号中的特征频率，是路径规划的实用方法，计算多重分形非趋势波动分析。( Wavelet packet analysis to extract vibration signal characteristic frequency, Is a practical method of path planning, Calculate the multifractal trend fluctuation analysis.)
- 2017-01-04 16:40:29下载
- 积分：1
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zhangwen
基于MATLAB实现指纹特征提取 改进的指纹图像分割算法 论文(matlab zhiwentezhengtiqu zhiwenfengesuanfa)
- 2010-05-05 23:09:09下载
- 积分：1
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nsys-analysis-technique-guide
ansys分析技术指南，详细介绍了拓扑优化和自适应网格划分(ansys analysis technique guide, detailing the topology optimization and adaptive meshing)
- 2015-01-06 10:41:21下载
- 积分：1
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menu4_4
菜单的仿真，通过修改菜单可以修改自己需要修改的参数，例如PID(Simulation menu, the menu can be modified by modifying the need to modify the parameters of their own, such as PID)
- 2014-01-04 18:02:05下载
- 积分：1
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toolbox_graph
主要用于对图论和三角测量的研究的工具箱，包含很多分析模拟网络拓扑的函数(mainly for the Graph Theory and Research Triangle measurement toolkit include many analyze the function of the network topology)
- 2007-05-21 10:55:57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
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IdiomsA
some idioms in english
- 2010-09-26 22:25:21下载
- 积分：1
