BP_Simplex

The Matlab Fuzzy Logic tool box

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)

fir: Transform lowpass filters to highpass

The smooth particle hydrodynamics (SPH), the diffuse element method (DEM), the element-free Galerkin method (EFGM), the reproducing kernel particle method (RKPM), the moving-particle semi-implicit method (MPS) are among others. However, it seems that these meshless methods have not succeeded in replacing the FEM analysis completely, while they show excellent performance in several special fields.

说明：  关于神经网络（matlab）归一化的见解(On Neural Networks (matlab) normalized view)

说明：  遗传算法的matlab实现程序 多个实例与多种计算方法(genetic algorithm to achieve Matlab procedures with multiple instances of multiple calculation method)

blcoh equation represetation of MRI phnonmenan

he files in this package comprise the Matlab implementation of a foreground segmentation algorithm based upon graph cuts, described in: Better Foreground Segmentation Through Graph Cuts, N. Howe & A. Deschamps. Tech report, http://arxiv.org/abs/cs.CV/0401017. The file extractForeground.m contains a sample function that will perform a complete foreground segmentation for static camera video. It uses a number of parameters, which are described in the documentation. An attempt was made to choose sensible default values, but they may need to be adjusted for some videos. More generally, the implementation in extractForeground.m is only a suggestion of how the graph-based segmentation might be used. The same approach can be applied with other (perhaps time-adaptive) background models. The key step is at line 113 of the code, once the deviation array has been created. The graph-cut segmentation can be applied similarly to a deviation matrix computed in any other way.

现代信号处理，语音处理，matlab程序，LMS算法(matlab lms speech)

LMS和RLS算法的对比，及其性能分析，研究其性能曲线的变化(Comparison of LMS and RLS algorithm and its performance analysis to study changes in the performance curve)