matlabrose

matlab程序设计源程序，图形显示功能，信科专业比较实用(matlab programming source code, graphics display capabilities, the letter more practical subjects of professional)

复变函数与积分变换的学习辅导和课后习题全解(Complex Variables and Integral Transform counseling and after-school learning all solutions of Problem)

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)

此程序根据戏典连续体结构拓扑优化的程序改编，可以实现多载荷多工况的结构拓扑优化(This program code according to opera continuum structural topology optimization procedure for adaptation can be achieved multi-multi-load status of structural topology optimization)

程序内容是对信号的波束域检测，其中阵元数32，信号为宽带信号(Program content is the signal beam field testing, which array element number 32, the signal is wideband signals)

说明：  传统PID控制matlab程序，希望对大家有所帮助，共同学习。(Traditional PID control matlab program, I hope all of you to help, learning together.)

说明：  高级数字信号处理AR模型信号处理 MATLAB 例程(the information process of model AR)

《Matlab图形和数据可视化》一书的附带源代码(MATLAB Graphics and Data Visualization Cookbook)

说明：  Beam width is the aperture angle from where most of the power is radiated. The two main considerations of this beam width are Half Power Beam Width (HPBW) and First Null Beam Width (FNBW).

用粒子群算法解决0/1背包问题，解决在背包体积有限的情况下，如何最大化的实现有效资源分配(Solve knapsack problem with particle swarm algorithm to solve the limited volume of the backpack, how to maximize the efficient allocation of resources to achieve)