RegsiterFM

《matlab时尚编程百例》work40-69()

基于matlab的车牌定位和分割，能够良好的分割和定位汽车车牌(Matlab-based license plate location and segmentation, to a good vehicle license plate segmentation and positioning)

To find the largest fitness value and its location 寻找最大适应性及相应的位置！Population N=50；crossover bits=n/2 (half of bits of an individual) with random locations，mutation bits = 4 种群数 N=50，交换位数= n/2, 即个体位数的一半，且位置随机； 变异位数统一取为4； Nc=20，28，36，44，individuals for crossover(交换的个数)。Nm=1，5，10，15， individuals for mutation(变异的个数)。(To find the largest fitness value and its location to find the location of the maximum adaptability and the corresponding! Population N = 50 crossover bits = n/2 (half of bits of an individual) with random locations, mutation bits = 4 population size N = 50, exchange digits = n/2, ie half of the median individual, and random position variation is taken as unity median 4 Nc = 20,28,36,44, individuals for crossover (exchange number). Nm = 1,5,10,15, individuals for mutation (the number of variations.))

基于mv准则的波束赋形算法，可以根据来波方向向三个方向形成波束(Mv criteria-based beamforming algorithm, according to wave direction to form a beam in three directions)

本文件包含常见的一些matlab函数的用法和功能，便于初学者者使用。(This file contains some of the common usage and function of matlab functions, for beginners to use.)

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)

关于雷达成像的一个经典参考资料,很有价值(On radar imaging of a classic reference)

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.)

Code for FIFO the first

简单演示了VC-Matlab混合编程的一种方法。(Simple demonstration of the VC-Matlab mixed programming methods.)