matlab 复杂网络工具箱 (free)下载

weixin_39821051 2019-07-15 09:30:27

Acknowledgements

This package, and especially the more advanced algorithms in it were developed in tight cooperation with Prof. Yoram Louzoun. Some of these algorithms were implemented by Royi Itschak.

Referencing

The Complex Networks Analysis Package is free for noncommercial use. If used for ac
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Acknowledgements This package, and especially the more advanced algorithms in it were developed in tight cooperation with Prof. Yoram Louzoun. Some of these algorithms were implemented by Royi Itschak. Referencing The Complex Networks Analysis Package is free for noncommercial use. If used for academic research, please, reference it as: Lev Muchnik, Royi Itzhack, Sorin Solomon, and Yoram Louzoun, Self-emergence of knowledge trees: Extraction of the Wikipedia hierarchies, Phys. Rev. E 76, 016106 (2007)

Matlab 的 Ezyfit 工具箱使您能够使用任意（非线性）拟合函数对一维数据执行简单的曲线拟合。该工具箱提供了一组命令行函数来“以编程方式”执行曲线拟合：您只需输入类似showfit('c+a/x^n') 的内容，EzyFit 就会为您提供c、a和n 的值并向您展示曲线！访问http://www.fast.u-psud.fr/ezyfit/了解更多信息。安装 1. 下载 EzyFit并将其解压到系统中的某个目录中。不要将工具箱直接安装在 Matlab 应用程序的目录中（Windows 中的Program Files/Matlab目录）。如果从旧版本升级，请先清空以前的目录。 2. 选择“设置路径”。在对话框中，单击“添加文件夹”（而不是 “包含子文件夹”）并选择ezyfit目录。单击“保存”和“关闭”。 3. 如果您希望始终在图形中显示 Ezyfit 菜单，请输入 efmenu install。这将在 Matlab 安装的主用户目录中创建或更新“ startup.m ”文件。如果升级 Matlab 并且想要使用之前安装的 Ezyfit，按照2-3重复操作。

http://www.mathworks.com/matlabcentral/fileexchange/25986-constrained-particle-swarm-optimization Description Previously titled "Another Particle Swarm Toolbox" Introduction Particle swarm optimization (PSO) is a derivative-free global optimum solver. It is inspired by the surprisingly organized behaviour of large groups of simple animals, such as flocks of birds, schools of fish, or swarms of locusts. The individual creatures, or "particles", in this algorithm are primitive, knowing only four simple things: 1 & 2) their own current location in the search space and fitness value, 3) their previous personal best location, and 4) the overall best location found by all the particles in the "swarm". There are no gradients or Hessians to calculate. Each particle continually adjusts its speed and trajectory in the search space based on this information, moving closer towards the global optimum with each iteration. As seen in nature, this computational swarm displays a remarkable level of coherence and coordination despite the simplicity of its individual particles. Ease of Use If you are already using the Genetic Algorithm (GA) included with MATLAB's Global Optimization Toolbox, then this PSO toolbox will save you a great deal of time. It can be called from the MATLAB command line using the same syntax as the GA, with some additional options specific to PSO. This will allow a high degree of code re-usability between the PSO toolbox and the GA toolbox. Certain GA-specific parameters such as cross-over and mutation functions will obviously not be applicable to the PSO algorithm. However, many of the commonly used options for the Genetic Algorithm Toolbox may be used interchangeably with PSO since they are both iterative population-based solvers. See >> help pso (from the ./psopt directory) for more details. Features * NEW: support for distributed computing using MATLAB's parallel computing toolbox. * Full support for bounded, linear, and nonlinear constraints. *

将无网格Galerkin法（element-free Galerkin method，EFGM）和改进的Madab遗传算法工具箱（matlab genetic toolbox，MGT）相结合，提出了一种新的连续体结构形状优化设计方法。针对形状优化设计的特点，建立了一种规则网格与有限元网格混合的EFG积分背景网格法。详细介绍了M函数文件的修改方法，解决了现有Madab遗传算法工具箱不能直接施加的非线性与隐性约束条件的难题。最后，完成了一个工程实例的结构形状优化，优化结果表明该方法是有效可行的。

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