readObj3

MATLAB中的各种函数的汇总，集合了MATLAB中所有的函数是一个比较全的的汇总资料，传上去供大家下载(MATLAB in a summary of the various functions, bringing together all of the function of MATLAB is a summary of the whole information, Chuan-up for all to download)

说明：  改程序为图像增加一个属性菜单，并轻松更改最近浏览图片的属性(IMAGEMENU, without any input arguments, will create this context menu for all images that are found in the current figure)

两点边值问题，微分方程数值解法，数值分析作业(Boundary Value Problems, Numerical Solution of Differential Equations)

说明：  《精通MTLAB最优化计算》第9章 非线性最小二乘优化问题 源码(&quot The most proficient MTLAB Optimization&quot Chapter 9 source nonlinear least squares optimization problem)

2000年数学建模B题 材料运输问题 MATLAB和LINGO程序代码(Mathematical modeling in 2000 B topic materials transportation problem and LINGO MATLAB program code )

K MEANS VERSİ ON NEW CLASFİ CATİ ON

matlab编写的数字签名言简意赅，相信一定会有人用到。(matlab digital signature prepared concise, I am sure someone will use.)

说明：  用Matlab实现中值滤波，均值滤波以及高斯滤波(Using Matlab to achieve median filter, mean filter and Gaussian filter)

单级倒立摆的模糊控制以及在MATLAB中的仿真倒立摆在研究双足机器人直立行走、火箭发射过程的姿态调整和直升机飞行控制领域中有重要的现实意义，相关的科研成果己经应用到航天科技和机器人学等诸多领域(A handstand on the study of bipedal robot to walk upright, rocket launch posture adjustment and in the field of helicopter flight control have important realistic meaning, related research achievement has been applied to many fields such as aerospace science and technology and robotics)

说明：  CA算法可以将数量型属性划分成若干个优化的区间，它综合了分层聚类于划分聚类的优点，对于给定的不同的初始类个数，CA算法能随着迭代过程的不断进展改变类的数目，一些竞争力差的类即类的基数小于给定阙值的类将在迭代过程中不断消失，最终得到能够有效体现数据实际分布情况的优化聚类个数。(CA algorithm for quantitative attributes can be divided into a number of the interval optimization, hierarchical clustering which combines the advantages of clustering in the division, for a given number of different types of initial, CA algorithm as the iterative process the continuous progress of change in the number of categories, some categories of poor competitiveness of the base type that is less than the value of a given category Que iterative process will continue to disappear and eventually be able to effectively reflect the actual distribution of data to optimize the number of clustering.)