Galois_Field_Computation_in_MATLAB

可实现Butterworth滤波器 的功能 使图像处理的一个重要功能部分(Butterworth filters can realize image processing functions to an important functional part)

MATLAB Mattrix LABoratory is an application that con et e ¸ cue to provide a simple matrix computing environment, efficient, interactive and mobile, allowing the implementation of algorithms in evelopp es projects Linpack and eispack.

此程序为关于卡尔曼滤波定位代码的matlab源程序(This paper presents a fuzzy adaptive Kalman filter technology )

One of the most popular adaptive algorithms available in the literature is the stochastic gradient algorithm also called least-mean-square (LMS) [1], [2]. Its popularity comes from the fact that it is very simple to be implemented. As a consequence, the LMS algorithm is widely used in many applications

让ARToolKit支持MQO模型的必须头文件，GLMetaseq.h(Let ARToolKit support MQO model must header files, GLMetaseq.h)

用于大坝安全预警模型的多元回归以及变量分离的程序。也可以用于其他非线性回归(Dam safety warning model for multiple regression and variable separation procedures. Can also be used for other non-linear regression.)

Using the steepest descent to solve do the optimization work. You can either cll the function or use the difference method to derive the gradient

object oriented handling example

说明：  Euler method for differential equation

蕞优分组周游路线问题是一类物流智能调度问题，是众多的计算难度极大的组合爆炸问题之一。自从运输问题最早是由Hitchcock在1941年提出的[2][4]后，人们对这一类问题给予了极大的关注并进行了大量的研究，提出了有效解决问题的优化算法——单纯形法的变形[2]。但由于问题规模的扩大，纯数学方法在允许的时间和空间上都很难找到问题的最优解，于是人们开始转向寻找问题的近似最优解，遗传算法以其在求解最优化问题中的独特的自组织性、自适应性很快进入人们的视野。 经典的遗传算法对于求解多目标的NP完全性问题非常有效，但对于有多个限制条件的多目标最优化问题缺显得有点力不从心，很难得到稳定度较高、收敛较快的解。本文提出的双层遗传算法模型给这一类问题提供了一个很好的解决方法，虽然建模条件限制了描述的问题的通用性，有待推广，但它能有效利用问题的领域知识，提高了求解效率，保证了解的可行性和最优性，算法思想极具推广潜力，可在其它物流智能调度问题求解算法设计中借鉴。(The most optimal packet travel route problem is a kind of intelligent logistic scheduling problem is one of combinatorial calculation difficult problem many. Since the transportation problem was first proposed by Hitchcock in 1941 after the [2][4], the people of this class of problems given great attention and made a lot of research, the deformation of [2] optimization, simplex method algorithm is effective to solve the problem of. But due to the expansion of the scale of the problem, the pure mathematical methods in the allowed time and space are very difficult to find the optimal solution of the problem, so people begin to turn to the approximate optimal solution for the problem, genetic algorithm in solving optimization problems with its unique in the self-organizing, adaptive soon entered people s field of vision. Very effective classical genetic algorithm for solving multi-objective NP complete problems, but for the multi-objective optimization problem with multiple constraints. I)