Chaotic-Systems-Toolbox

计算一个平面桁架结构的总体刚度矩阵，最后以图形形式显示出桁架结构(Calculation of a plane truss structure' s overall stiffness matrix, the last show in graphical form of truss structure)

一本讲述时间序列分析的英文书，非常详细！(A book about time series analysis of the English book, very detailed!)

hough变换实现直线检测，MATLAB实现，带实例说明(hough transform)

请输入非递减的有序序列,输入-1结束,再输入一个数字，插入其中，按照从小到大的顺序进行排列，完成这个程序(Please enter a non-decreasing ordered sequence, enter-1 end, and then enter a number, into which, in ascending order of arrangement, complete this program)

这是关于高阶统计量的算法源程序，内容比较全面，大家可以认真看一看(This is on the higher-order statistics of the algorithm source code, content more comprehensive, we can seriously look at the)

helps in maximal ratio combining,equal gain combining,snr improvement,select diversity

说明：  a m file for matlab the secant method(mathlab)

若不希望用与估计输入信号矢量有关的相关矩阵来加快LMS算法的收敛速度，那么可用变步长方法来缩短其自适应收敛过程，其中一个主要的方法是归一化LMS算法（NLMS算法），变步长 的更新公式可写成 W（n+1）=w（n）+ e（n）x（n） =w（n）+ （3.1） 式中， = e（n）x（n）表示滤波权矢量迭代更新的调整量。为了达到快速收敛的目的，必须合适的选择变步长 的值，一个可能策略是尽可能多地减少瞬时平方误差，即用瞬时平方误差作为均方误差的MSE简单估计，这也是LMS算法的基本思想。 (Want to estimate if the input signal vector and the relevant matrix to speed up the convergence rate of LMS algorithm, then the variable step size method can be used to shorten its adaptive convergence process, one of the main method is normalized LMS algorithm (NLMS algorithm) , variable step-size update formula can be written W (n+ 1) = w (n)+ e (n) x (n) = w (n)+ (3.1) where, = e (n) x (n) the right to express filter update vector iterative adjust the volume. In order to achieve the purpose of fast convergence, we must choose the appropriate value of variable step size, a possible strategy is as much as possible to reduce the instantaneous squared error, which uses the instantaneous squared error as the mean square error MSE of the simple estimate, which is the basic LMS algorithm思想.)

关于无穷大电源的一个仿真模型，模型很简单，但是很实用。(About the infinite power of a simulation model, the model is very simple, but very practical.)

The Support Ve ct or Machi ne So fa r we have used a reference as sumpt ion tha t there exists a linear classifier that has a larg e ge ometric margin, i.e., whose decision b oundar y is we ll separa ted from all the training images (e xamples). Such a larg e margin classifier seems like one we would like to use. C an’t we find it more directly? Yes, we can. The clas sifier is known as the Support Ve ctor M achine or SVM for short.