kounie_v59

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)

求解绳索单元在均布荷载下的挠度 利用有限元方法 matlab编程(haha hehe heihei qiekenaoqiekenao)

基于字典学习的超分辨率例程，matlab编辑，对于理解超分辨率有一定帮助。(Dictionary-based super-resolution routine learning, matlab editor, for understanding the super-resolution of some help.)

说明：  通过遗传算法优化BP神经网络，提高其收敛速度和预测准确度。(BP neural network is optimized by genetic algorithm to improve its convergence speed and prediction accuracy.)

说明：  对高光谱，拉曼光谱可以做特征提取的思路，先做案例，搞懂后再移植到自己的实验里。(For hyperspectral, Raman spectrum can do feature extraction ideas, first do cases, understand and then transplant to their own experiments.)

一种通用的稀疏编码方法，sparse coding，在图像去噪，目标识别等方面有着广泛的应用。(A generic sparse coding, sparse coding, in image denoising, object recognition have a wide range of applications.)

matlab 算法 (matlab algorithm)

steepest golden section method optimization methods and algorithms

阵列信号处理的高分辨率估计，有CDF三角函数曲线/三维曲线图，能量熵的计算，Matlab实现界面友好，结合PCA的尺度不变特征变换(SIFT)算法，调试通过可以使用。( High-resolution array signal processing estimates, There CDF trigonometric curve/3D graphs, Energy entropy calculation, Matlab to achieve user-friendly, Combined with PCA scale invariant feature transform (SIFT) algorithm, Debugging can be used.)

说明：  遗传算法求解VRPTW,可运行，附带说明(VRPTW-GA Genetic Algorithms for VRPTW Solution, Running, with Description)