BUTTERWORTH

模拟离轴彩色数字全息图的MATLAB程序(MATLAB program of simulated off axis color digital hologram)

源程序使用matlab编写 经过运行后 可以产生卡农音乐(it can produce canon music)

说明：  神经网络进行产量的Matlab源代码 BP 神经网络用于预测 (Neural network output of Matlab source code for BP neural network to predict)

仿真12脉冲直流提调速系统之一，12脉冲整流matlab仿真(Simulation of 12-pulse DC mentioning governor system one.rar)

The Program helps to remove numbers at the beginning of a Matlab script. It is very useful when one has copied a code from a text into Matlab environment and eager to tun it immediately.

基于simulink建立了飞机六自由度模型，并计算其迎角。(Based on simulink established six degrees of freedom model aircraft, and calculate the angle of attack.)

本书适合不同阶段的MATLAB学习者，包括矩阵的计算，图形的绘制和建模，对广大CAGD爱好者很有帮助(Book is suitable for learners at different stages of MATLAB, including matrix calculations, graphics rendering and modeling, useful for the majority of fans CAGD)

微分进化算法可以求解大规模非线性的数学模型(Differential evolution algorithm)

这是一个自己编写的MATLAB函数，是DFT的快速算法，是一种基于2FFT算法(This is a self-written MATLAB function is fast DFT algorithm, a method based on 2FFT)

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)