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intesmc
一种带有积分环节的滑模变结构控制方法程序，包含.mdl与S-function，，(Of a link with the integral sliding mode variable structure control methods and procedures, including. Mdl with the S-function,,)
- 2020-07-22 16:08:44下载
- 积分：1
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Untitled
说明： 属于一群算法，用于通用性解决最短路径问题，方便快捷。(Belongs to a group of algorithms for solving shortest path problem versatility, convenience)
- 2010-05-02 07:38:38下载
- 积分：1
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firefly_algorithm
萤火虫算法（Firefly Algorithm）是一种群智能优化算法，它是英国的学者在2008年提出的，其本质是利用萤火虫在觅食和信息交汇中的一种仿生算法。(
Firefly Algorithm (Firefly Algorithm) is a swarm intelligence optimization algorithm, which is a British scholar proposed in 2008, and its essence is to use the firefly in the convergence of information and foraging in a bionic algorithm.)
- 2016-05-14 22:21:30下载
- 积分：1
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zigZag_matab
zigZag scan implementation on matlab
- 2009-03-27 09:48:25下载
- 积分：1
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DeskGGD3top
【谷速软件】误差边界的绘制 可以作为参考使用学习下([Valley] draw speed software error bound can be used as a reference use under study)
- 2014-12-21 21:48:17下载
- 积分：1
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fenleiqi
基于Matlab的分类器程序，采用最小误差判别准则(Classification program which based on Matlab, minimum error criterion)
- 2012-10-23 18:21:07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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complex
assigning complex numbers
- 2013-03-04 12:12:06下载
- 积分：1
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Finite-element-MATLAB
说明： MATLAB有限元分析与应用，适用于matlab编程研究人员(Finite element analysis and application of MATLAB, matlab Programming for Researchers)
- 2011-03-13 09:51:52下载
- 积分：1
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Blind-deconvolution
模糊图像处理 盲解卷
处理模糊图像
matlab程序(Blind deconvolution)
- 2011-05-16 21:48:11下载
- 积分：1
