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Greedy Algorithms for Underdetermined but Sparse Solutions

Tutorial on maximum likelihood estimation

粒子群优化算法（PSO）应用于神经网络优化程序。分为无隐含层、一隐含层、二隐含层。运行DemoTrainPSO.m即可(Particle Swarm Optimization (PSO) used neural network optimization program. Divided into no hidden layer, a hidden layer, two hidden layers. You can run DemoTrainPSO.m)

考虑Jeffcott转子系统运动微分方程的newmark-belta法matlab程序(Consider Jeffcott rotor system movement differential equation method of newmark- belta matlab program)

本程序是快速独立成分分析算法中需要调用的一个很重的子程序。(This procedure is fast independent component analysis algorithms need to call a heavy subroutine.)

multi-objective optimization

用matlab分析典型的洛伦茨动力系统的性质，讨论混沌分型等非线性系统的性质。(Matlab analysis with the typical nature of the Lorenz dynamical system discussed such chaos type properties of nonlinear systems.)

its code to reduce papr

设r是f(x) = 0的根，选取x0作为r初始近似值，过点（x0,f(x0)）做曲线y = f(x)的切线L，L的方程为y = f(x0)+f (x0)(x-x0)，求出L与x轴交点的横坐标 x1 = x0-f(x0)/f (x0)，称x1为r的一次近似值。过点（x1,f(x1)）做曲线y = f(x)的切线，并求该切线与x轴交点的横坐标 x2 = x1-f(x1)/f (x1)，称x2为r的二次近似值。重复以上过程，得r的近似值序列，其中x(n+1)=x(n)－f(x(n))/f (x(n))，称为r的n+1次近似值(Let r is f (x) = 0 root, select the initial approximation x0 as the r, over point (x0, f (x0)) to do the curve y = f (x) the tangent L, L the equation y = f ( x0)+ f ' (x0) (x-x0), find the intersection of L and the x-axis of abscissa x1 = x0-f (x0)/f' (x0), x1 is called an approximation r. Through points (x1, f (x1)) to do the curve y = f (x) the tangent, and find the intersection of the tangent with the x-axis of abscissa x2 = x1-f (x1)/f ' (x1), x2 is called r the second approximation. Repeat the process, get an approximation of the sequence r, where x (n+1) = x (n)-f (x (n))/f ' (x (n)), as an approximation of r n+1 times)

产生一定载波频率、重复频率及脉冲间隔的高斯形脉冲信号(Produce gaussian signal with certain repetition frequency, the carrier frequency and pulse interval)