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SOURCE MATLAB MERCI A TOUS LE MONDE

基于熵权的水资源短缺风险模煳综合评价模型及应用(Entropy-based model of water shortage risks burnt comprehensive evaluation model and application)

matlab 中用Bisection方法求解一元多次方程，是一种很好的算法。但收敛速度比较慢，是线性收敛(matlab using Bisection method for solving multiple equations one yuan, is a good algorithm. But convergence is slow, linear convergence)

在非合作博弈中，2013年提出的kg-cp算法的仿真程序(In the non-cooperative game, 2013 kg-cp proposed algorithm simulation program)

matlab for plot the graph lecture note

关于机器人舒适度区域的研究文章，外国人写的。(Comfort zone on the robot research articles written by foreigners.)

《有限单元法及应用》最新2012版。公式推导详尽，matlab符号运算及算法简单易懂，理论介绍与软件应用连接紧密，适合作为非力学专业高年级本科生及研究生教材使用，也可作为有限元应用领域内工程技术人员的参考书。(The latest 2012 edition of the finite element method and its application " . The formula derivation detailed, the Matlab symbolic computation algorithm is simple and easy to understand, the theory describes the software application connections are tight, suitable as a non-mechanical professional senior undergraduate and graduate textbook, and may also be used as a reference book of engineering and technical personnel in the field of finite element applications.)

GAUSS滤波器 MATLAB语言 本科毕业设计需要(GAUSS filter)

Matlab实现绘制任意自由曲线，实现Photoshop中的钢笔路径功能，用光顺连接的B样条/贝塞尔曲线实现，鼠标点击生成控制点，拖动形成任意曲线。可描绘多路径，也可旋转、平移、缩放。经调试可用。(Matlab achieve rendering arbitrary free curve, realize the pen path function in Photoshop, with the light of the B spline/Bessel curve, the mouse click to generate control points, drag to form any curve. Can depict many paths, but also can rotate, translation, scaling. Debug available.)

实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。(achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or))