tupianchuli

说明：  遗传算法多目标优化的应用举例，求非线性函数最大值。(Multi-objective optimization genetic algorithm application example, the maximum value of nonlinear function.)

随机粗糙表面对微气体轴承内气体压强分布的影响(Pressure distribution in mircro gas bearing with rough surface)

资料中包含利用校验子（错误图样）译码的原理、编码流程图以及matlab的编码，程序中配有注释，通俗易懂。(Information contained in the use of sub-parity (error pattern) decoding principle, flow charts and coding matlab code, the program with notes, easy to understand.)

Simulink 控制VR环境中的小车。小车有5个距离传感器，能够慢慢学会避开墙壁和障碍物。小车采用加强学习（Q learning），采用神经网络对Q函数逼近。由于使用了模拟退火，小车在开始的时候会经常撞击障碍物，10次后基本就不会再撞了。 小车的外观模型使用了"w198406141"在本论坛的虚拟现实区发布的VR模型。(VR environment Simulink control car. There are 5 car distance sensor, can gradually learn to avoid walls and obstacles. Car used to enhance learning (Q learning), using neural networks Q function approximation. As the use of simulated annealing, the car will start when the regular crash barrier, 10 times the fundamental will no longer hit. The appearance model used car " w198406141" in this forum area of virtual reality VR model release.)

说明：  有限元的四边形剖分程序，可用于有限元求解磁场，电场等问题是的区域划分中(fem use to answer EMT )

surface chart Based on particle swarm optimization画图法(surface chart Based on particle swarm optimization)

m序列产生m序列产生m序列产生m序列产生m序列产生m序列产生(m-sequence generator m sequence generation m-sequence generator m-sequence generator m sequence generation m-sequence generator)

说明：  通过CFAR算法实现船舶目标检测，能够得到很好地效果。(Ship target detection by CFAR algorithm)

本程序为《结构分析的有限元法与Matlab程序设计》第三章的第一个算例，采用平面梁单元计算平面刚架的变形和内力，已经过修改。(This program is "structural analysis of the finite element method and Matlab Programming" chapter of the first example, the use of plane beam element of plane frame deformation and internal forces, which have been modified.)

说明：  SGA（Simple Genetic Algorithm）是一种强大的智能多变量优化算法，它模仿种群繁殖规律来进行优化。本SGA可以优化变量，求最小值,最大值（当把函数倒数也就求最小值） 并且支持浮点编码，grey编码，二进制编码；轮赌法选择，锦标赛选择；单点交叉，均布交叉，浮点交叉；单点变异，浮点变异；调用时Genetic（目标函数名） 使用SGA时，首先需要一个目标函数（像AimFunc.m），该函数返回适应度输入变量为待优化变量x输出为一个适应度。然后修改Genetic.m中可以修改的地方(SGA (Simple Genetic Algorithm) is a powerful smart multi-variable optimization algorithm, which mimics the reproduction of law to be optimized. The SGA can be optimized variables, minimum, maximum, (when the function will last for the minimum) and supports the floating-point encoding, grey code, binary code round of betting method selection, tournament selection single-point crossover, uniform crossover , floating-point crossover single point mutation, floating-point mutation called Genetic (objective function name) the use of SGA, first of all need an objective function (like AimFunc.m), the fitness function returns the input variables to be optimized for the output variable x a fitness. Can be modified and then modify the place Genetic.m)