tulunzuiduanlumatlabcode

lab exercise programs to learn matlab. easy examples to know the basic functions used in matlab coding. we can also learn how to create a circle ,spiral etc...

数值代数常用变换，内部含有Gauss，cholesterol，household，givens,QR分解等变换(Common transform numerical algebra, contains an internal Gauss, cholesterol, household, givens, QR decomposition transformation)

一个bp的逼近算法(an approximation algorithm bp)

Program to do main program 2 with picture Lena

单值分类，包括很多的工具函数，使用时直接调用就可，用起来相当方便的，欢迎大家下载(svdd anewmethodofone2classclassificationofmechanicalfault2supportvectordatadescriptionwasproposedinwhichtheone2classclassifiercanbesetupbyusingonlyonekindoftargetsamplewithoutknowingotheroutliersamples)

GM（1，1）模型的MATLAB源代码2(GM model)

MATLAB环境下的模型预测控制理论的应用(MATLAB environment, the application of model predictive control theory)

[自動控制]Time domain 2 norm 計算程式。輸入system function(由TF, ZPK, SS, FDS建立)，程式會自動計算2-norm.(NORM2(G) computes the 2-norm, i.e. the RMS value, of a strictly proper stable system G (created with either TF, ZPK, SS, or FRD). || [RMS_VALUE, AUG_ROUTH_TABLE]=NORM2(G) returns the RMS value and the augmented routh table of the given system. || Get the numerator and denominator of the system transfer function )

说明：  神经网络 侯媛彬 西安电子科技大学出版社 全书程序夹CH4(Neural network)

本文将排课五要素绑定、封装成课元、课栈两个向量，并以二维权值矩阵描述课元 和课栈之间的关联系。 提出了最佳匹配和遗传算法的排课算法，以随机生成的二维数字矩阵来反映课元和 课栈的关联权重，对数字矩阵组处理和最佳匹配得到的匹配偶图组为遗传算法的初始种 群，以排课各种原则的加权和为目标函数，在遗传算子控制下迭代来寻求更优的匹配结 果即使目标函数最小的偶图匹配结构，进而得到满意的排课结果。 提出了关联匹配和禁忌搜索的排课算法，以随机二维数字矩阵描述课元和课栈之问 的关联权重，按照重权优先并根据正确性原则进行约简，获得课元和课栈之间的匹配偶 图。在此基础上，引入禁忌搜索算法，以排课的完备性原则、合理性原则和人性化原则 的加权和为目标函数，以随机二维权值矩阵生成的匹配偶图为初始解，对二维权值矩阵 进行扰动构成搜索邻域，以生成的匹配偶图为禁忌对象，采用全局藐视准则，获得最优 的排课方案。 以Matlab为平台，利用Excd数据库和Mallab的互联实现数据交换，编写了两套 算法的应用程序，通过实例来验证两套排课算法，结果表明两套算法是可行的。(Arranging the five elements of this paper, binding, packaging into a class element, class stack of two vectors and matrices described by two classes per Rights And the relationship between class stack link. Offers the best match and Genetic Algorithm Platoon algorithm to randomly generated two-dimensional figures to reflect the class element and matrix Class stack associated with weight, the digital matrix group received treatment and the best matching group bipartite graph matching the initial kind of genetic algorithm Group to the weighted course arrangement of principles and the objective function, under the control of the genetic operator iteration to seek the better match Results Results even if the objective function structure of the smallest bipartite graph matching, and then Timetabling satisfactory results. Match the proposed association Timetabling algorithm and tabu search, a random two-dimensional digital matrix element and the class description class stack of Q)