Des3321ktop

深度优先搜索算法Matlab源码 ％ 此程序从国外网站收集得到，是标准的深度优先搜索算法，可实现节点遍历和检测回路的功能，详细情况请看原英文注释 ％ 我在此程序中添加了随机性，即遇到分叉时，随机选下一个节点，成为随机深度优先搜索算法(Depth-first search algorithm Matlab source this procedure site collected from abroad, is the standard depth-first search algorithm, can be achieved and the detection circuit node traversal functions, details see the original English Notes I added this procedure randomness, that is, bifurcation encountered when a randomly selected node, become a random depth-first search algorithm)

Matlab编程实现简单的流体替换以及前后合成地震记录对比，含word文档及图示说明。(Matlab programming simple fluid replacement and synthetic seismograms before and after comparison, including word documents and illustrations.)

这里给出几幅多聚焦图像，为大家做图像融合处理，一共两组图像(Here are the pieces of multi-focus image, for everyone to do image fusion, a total of two sets of images)

说明：  用DCT算法做数字水印，水印为伪随即序列。对整个图做DCT变换(DCT algorithm used as a digital watermark, watermark for the pseudo-then sequence. The entire plan to do DCT)

说明：  热交换器用于物理热学研究计算使用用于仿真实验(Heat exchanger heat exchanger is used in physical heat research and calculation)

变步长灰色预测的配浆浓度模糊控制研究Variable step with Grey Prediction Fuzzy Control of plasma concentrations(Variable step with Grey Prediction Fuzzy Control of plasma concentrations)

matlab models really useful

this is a good simulation of fxlms ANC which contain both offline and onilne simulation

《神经模糊预测控制及其matlab实现》（第3版）matlab程序(Neuro fuzzy predictive control and its matlab implementation (Third Edition) matlab program)

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.)