nelder

Get kernell 32 in Windows

维纳匹配滤波器，在信号处理中，为了判断信号什么时候发生，维纳匹配滤波可以很好的检测信号是否发生，并在什么时候发生(wiener match fliter)

恒虚警门限求解，其中包括该程序所要调用的两个函数(CFAR threshold solution, including the procedure to call the two functions)

this are the example codes from control system engineering from NORMAN NISE

本代码通过对基因库中人类和家牛基因编码部分的对比和统计,分析了不同属生物间的基因遗传的异同和特性(This part of the code by comparing the gene encoding the human and cattle library chain genes were compared and statistics, and then to analyze the similarities and differences between different genera of biological and genetic characteristics of genetic)

complete text extraction for text window

包含了雷达系统中线性调频信号的时频仿真,匹配滤波和去调频两种压缩算法以及回波信号的模拟 (When the system contains a radar frequency chirp signal simulation, and the simulation algorithm and matched filtering and echo signal to FM two compression)

交错网格声波方程正演模拟，交错网格有限差分地震波场计算，二维的声波正演，利用空间10阶，时间2阶。(The CalCulation of the SeismiC Wave一 fieldwith Staggered-grid Finite difference Sheme)

用共轭梯度法求解x1^2+4*x2^2.精度为0.1(The Conjugate Gradient Method x1 ^ 2+4* x2 ^ 2. Accuracy of 0.1)

蕞优分组周游路线问题是一类物流智能调度问题，是众多的计算难度极大的组合爆炸问题之一。自从运输问题最早是由Hitchcock在1941年提出的[2][4]后，人们对这一类问题给予了极大的关注并进行了大量的研究，提出了有效解决问题的优化算法——单纯形法的变形[2]。但由于问题规模的扩大，纯数学方法在允许的时间和空间上都很难找到问题的最优解，于是人们开始转向寻找问题的近似最优解，遗传算法以其在求解最优化问题中的独特的自组织性、自适应性很快进入人们的视野。 经典的遗传算法对于求解多目标的NP完全性问题非常有效，但对于有多个限制条件的多目标最优化问题缺显得有点力不从心，很难得到稳定度较高、收敛较快的解。本文提出的双层遗传算法模型给这一类问题提供了一个很好的解决方法，虽然建模条件限制了描述的问题的通用性，有待推广，但它能有效利用问题的领域知识，提高了求解效率，保证了解的可行性和最优性，算法思想极具推广潜力，可在其它物流智能调度问题求解算法设计中借鉴。(The most optimal packet travel route problem is a kind of intelligent logistic scheduling problem is one of combinatorial calculation difficult problem many. Since the transportation problem was first proposed by Hitchcock in 1941 after the [2][4], the people of this class of problems given great attention and made a lot of research, the deformation of [2] optimization, simplex method algorithm is effective to solve the problem of. But due to the expansion of the scale of the problem, the pure mathematical methods in the allowed time and space are very difficult to find the optimal solution of the problem, so people begin to turn to the approximate optimal solution for the problem, genetic algorithm in solving optimization problems with its unique in the self-organizing, adaptive soon entered people s field of vision. Very effective classical genetic algorithm for solving multi-objective NP complete problems, but for the multi-objective optimization problem with multiple constraints. I)