conductor2

说明：  遗传算法车间调度,车间作业调度问题遗传算法 -------------------------------------------------------------------------- 输入参数列表 M 遗传进化迭代次数 N 种群规模(取偶数) Pm 变异概率 T m×n的矩阵，存储m个工件n个工序的加工时间 P 1×n的向量，n个工序中，每一个工序所具有的机床数目 输出参数列表 Zp 最优的Makespan值 Y1p 最优方案中，各工件各工序的开始时刻，可根据它绘出甘特图 Y2p 最优方案中，各工件各工序的结束时刻，可根据它绘出甘特图 Y3p 最优方案中，各工件各工序使用的机器编号 Xp 最优决策变量的值，决策变量是一个实数编码的m×n矩阵 LC1 收敛曲线1，各代最优个体适应值的记录 LC2 收敛曲线2，各代群体平均适应值的记录 最后，程序还将绘出三副图片：两条收敛曲线图和甘特图（各工件的调度时序图）(Genetic algorithm scheduling, job shop scheduling problems with genetic algorithms -------------------------------------------------------------------------- genetic evolution of the input parameter list, the number of iterations M N population size (taken even) Pm mutation probability T m × n matrix, stored m one piece n a process of processing time 1 × n vector, n a process in which each machine processes the number of Zp with the best value Y1p Optimal Makespan programs, the start time of each process the workpiece can be drawn based on its optimal solution Gantt Y2p, each time the workpiece end of the process, according to its draw Gantt Y3p optimal solution, each piece of the processes using machine code Xp optimal decision variable, decision variable is a real m × n matrix encoded LC1 convergence curve 1, the generation of the best individual record of LC2 fitness convergence curve 2, the average fitness value on behalf of groups record Finally, the program will draw three pict)

旋转交错网格无匹配层有限差分，应力计算速度部分(Rotated staggered grid finite difference without matching layer, the stress part of the calculation speed)

This code is used for find the Spectral magnitude error....and Spectral phase error....

numerical analysis method(newton method)

说明：  Kmeans实现聚类分析，还有一个用模拟退火优化的Kmeans。(Kmeans implements clustering analysis.)

Matlab数字图像处理学习笔记--从无到有学习笔记,适合新手学习 (Matlab Image Processing Study Notes- Study Notes from scratch, suitable for novice to learn)

双谱法时延估计算法，经过仿真验证效果很好(Dual spectroscopy delay estimation algorithm through simulation works well)

LMS自适应滤波器，实现信号的自适应滤波作用(LMS adaptive filter, adaptive filter realization of the role of signal)

高斯过程是一种非参数化的学习方法，它可以很自然的用于regression，也可以用于classification。本程序用高斯过程实现分类！(Gaussian process is a non- parametric method of learning, it is very natural for regression. can also be used for classification. The procedures used to achieve classification Gaussian process!)

核密度估计，可以刻画变量的时间演进动态性，可以反映变量在时间上的变动的整体性( Kernel Density Estimation )