ArrayCalc2_4_2

ga优化神经网权值&阈值程序。为了方便，我把几个matlab的程序都放在txt文件里，使用时，对照把txt内容复制进matlab即可(ga optimization neural network weights & threshold procedures. For convenience, I have several matlab programs on the txt file, use, control the content of the txt you copied into matlab)

evaluates a linear basis function and computes L2 and H1 error for 1D finite element method

this file is about algorithm string matching which is use matlab simulation.

该代码为电力系统区域间电力和能量交换，联络线功率控制的经典程序。(the code is made for power system energy and power exchange.)

找指紋特徵點 並圈選出來的 matlab 程序(Matlab program to find the fingerprint feature points and circle out)

通过MATLAB仿真工具箱simulink实现模糊控制原理下的PID智能控制器(Toolbox through MATLAB simulation simulink realize fuzzy PID control theory under the intelligent controller)

测试可以跑，根据自己情况修改下函数即可. NSGA-III 首先定义一组参考点。然后随机生成含有 N 个（原文献说最好与参考点个数相同）个体的初始种群，其中 N 是种群大小。接下来，算法进行迭代直至终止条件满足。在第 t 代，算法在当前种群 Pt的基础上，通过随机选择，模拟两点交叉（Simulated Binary Crossover，SBX）和多项式变异 产生子代种群 Qt。Pt和 Qt的大小均为 N。因此，两个种群 Pt和 Qt合并会形成种群大小为 2N 的新的种群 Rt=Pt∪Qt。 为了从种群 Rt中选择最好的 N 个解进入下一代，首先利用基于Pareto支配的非支配排序将 Rt分为若干不同的非支配层（F1，F2等等）。然后，算法构建一个新的种群St，构建方法是从 F1开始，逐次将各非支配层的解加入到 St，直至 St的大小等于 N，或首次大于 N。假设最后可以接受的非支配层是 L层，那么在 L+ 1 层以及之后的那些解就被丢弃掉了，且 St FL中的解已经确定被选择作为 Pt+1中的解。Pt+1中余下的个体需要从 FL中选取，选择的依据是要使种群在目标空间中具有理想的多样性。(The test can run and modify the function according to its own situation. NSGA-III first defines a set of reference points. Then the initial population containing N individuals (preferably the same number of reference points as the original literature) was randomly generated, where N was the size of the population. Next, the algorithm is iterated until the termination condition is satisfied. On the basis of current population Pt, the algorithm simulates two-point crossover (SBX) and polynomial mutation to produce offspring population Qt by random selection.)

An example of inverse kinematics algorithm

bpsk modulation in matlab

说明：  用MATLAB实现AHP层次分析法，有具体实际讲解及PPT说明(AHP based on the MATLAB, you can learn it from the PPT and we have the example.)