资源列表
dtw
- DTW算法，比较简单。在matlab下实现的，在matlab6。5下编译通过-DTW, relatively simple. Under the Matlab, in matlab6. Compiling under through five
examle
- Matcom是mathTools公司推出的一个能将M文件转化成相同功能C++代码的工具。相比Matlab自带的编译器Compiler，用Matcom转化代码要简单和方便得多。本文将结合作者编程经验，以VC6.0和Matcom为例，详细介绍如何利用Matcom进行VC与Matlab的联合编程。-Matcom is mathTools Company introduced a document can M into the same functional C code tool. Compared
pc_evectors
- 用matlab编写的找两个输入量之间的主要特征向量,主要方法采用PCA经典算法.-using Matlab prepared for the volume between two input of the main features of Vector, mainly using PCA classical algorithm.
matlab_api
- 这是学习matlab的好的程序，希望大家转载-This is a good learning Matlab procedures in the hope that we reprinted
mfiles
- 同上，学习matlab的好教程，呵呵，大家不要骂我亚-ibid., Matlab good learning guides, huh, I do not criticize Asia
digital transmission
- 这是一个完整的数据传输过程的matlab例程!-This is a complete data transmission process Matlab routines!
work
- 计算电波传播的一个MATLAB程序,用抛物线法求解的..还是初始阶段,共享一下.-terms of a radio wave propagation MATLAB procedures used parabola method .. or the initial stage, sharing about.
mhzq
- 用matlab 实现的图像模糊增强 在matlab 6.5 7.0sp1都可以用-using Matlab achieve the fuzzy images to enhance the Matlab 6.5 can be used 7.0sp1
MATLAB命令大全
- 本书提供 matlab 环境下的各种命令，包括详细的参数介绍。-book Matlab environment under the various orders, including the detailed parameters introduced.
pso toolbox
- 基于MATLAB的微粒群工具箱，算法模型中引入收缩因子，收敛速度有所提高，但对高维函数的优化效果仍然不理想。-based on MATLAB Toolbox PSO algorithm model introduced shrinkage factor, convergence rate increase, but for high-dimensional optimization function effects are still not ideal.
spso
- 保证全局收敛的随机微粒群算法。当最优粒子的解无进化，则对其位置、速度进行变异，而使算法不致过早收敛，只要迭代次数足够，算法保证全局收敛。-ensure global convergence of random particle swarm algorithm. When the optimal solution without particles of evolution, its location, speed variation, which might not premature conv
clusters
- 协同微粒群算法，用于原子簇的结构优化。协同微粒群算法模型由南非人Frans van den Bergh提出，对高维问题处理的效果要显著优于基本微粒群算法。-synergies PSO algorithm, for the cluster of structural optimization. Synergy PSO algorithm model from South Africa who Frans van den Bergh, for the high-dimensional problem