非对称全桥谱控制的贡献

说明：  宽带、窄带AR模型，对于不同的N,P,SINR的谱估计的对比分析(Broadband, narrowband AR model, for different N, P, SINR Comparison of spectrum estimation)

Face detection using matlab

It is matlab code for Steepest Descent optimization method.

说明：  主动频率偏移法，是用来检测孤岛效应的。采用S函数描述(Active frequency shift method is used to detect island effect. Function described by S)

direct torque documentation

拉丁超立方抽样试验设计程序：输入多维变量样本和所需组数，输出基于拉丁超立方抽样方法的实验设计结果(LHS test design program:input the multidimensional variable samples and the number set, output the design of experiment results based on Latin hypercube sampling method)

％File: c9_estimatepi.m ％有5个pi的估计，每一个都是基于500次重复随机试验，所得的pi的五个估计值用以下向量表示 ％pi的估计值=[3.0960 3.0720 2.9920 3.1600 3.0480] ％如果对5个结果进平均，则pi的估计值=3.0736,这样的结果等价于2500次的试验结果。- File: c9_estimatepi.m have 5 pi estimates are based on a randomized trial to repeat 500 times the income of the five estimated value of pi with the following vector, said the estimated value of pi = [3.0960 3.0720 2.9920 3.1600 3.0480] if the results into an average of 5, then the estimated value of pi = 3.0736, so the result is equivalent to 2500 times the test results.

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)

系统辨识 matlab源程序 最小二乘辨识(systems identification)

IP1101 circuit application