shuju_chuli

一个关于混沌序列的噪声压缩程序，主要基于主分量分析方法，消噪效果非常好。模拟信号上加了100 ％噪声(a chaotic sequence of the noise compression procedures, mainly based on principal component analysis, denoising very good results. Analog signals on the 100% increase in noise)

SAP2000软件的源程序，带有实例，可以进行静力分析、振型分析、反应谱分析、时程分析、曲屈分析、移动荷载分析、稳态分析、功能谱密度分析、静力Pushover分析、施工顺序加载分析等。(SAP2000 software source code, with examples, can be static analysis, modal analysis, response spectrum analysis and time history analysis, buckling analysis, moving load analysis, steady-state analysis, spectral density function analysis, static Pushover analysis, construction sequence load analysis.)

描述一种信道，通过卡尔曼滤波器对信道信号进行估计(A channel described by the Kalman filter to estimate the channel signal)

灰色关联度矩阵的算法，有一定的可取性,替换数字即可，用法简单。(The algorithm of grey correlation degree has certain desirability.)

基于matlab的相场案例，希望对各位有所帮助，如有不对，还请批评指正(Matlab-based phase-field case, they hope to be helpful, if wrong, criticism and)

FCM算法是一种基于划分的聚类算法，它的思想就是使得被划分到同一簇的对象之间相似度最大，而不同簇之间的相似度最小。模糊C均值算法是普通C均值算法的改进，普通C均值算法对于数据的划分是硬性的，而FCM则是一种柔性的模糊划分(FCM algorithm is a clustering algorithm based on the division, which makes the idea is to be divided into clusters with the greatest similarity between the object, and the similarity between different cluster minimum. Fuzzy C-means algorithm is an ordinary C-means algorithm, the general C-means algorithm for the data are divided into hard and fast, and FCM is a soft fuzzy partition)

考虑了基站通过多个中继与多个用户的问题，基于背包问题提出了最优功率分配。(the optimal power allocation is presented based on knapsack problem for a system with a base station communication with multiple users assisted by multiple relay.)

变分模态分解,可以用于信号处理，非常实用( U53D8 u5206 u6A21 u6001 u5206 u89E3, u53EF u4EE5 u7528 u4E8E u4FE1 u53F7 u5904 u7406 uFF0C u975E u5E38 u5B9E u7528)

单极性脉冲调制方式，三角载波在一种极性内变化(Unipolar pulse modulation, triangular carrier changing in one polarity)

说明：  常见的图像处理matlab源代码！！包括反色，常见滤波，去噪，和常用变换！(common image processing Matlab source code! ! Anti- including color, common filtering, noise removal, and common Transform!)