LFMRadar

无人机高度基于matlab数据融合（考虑卡尔曼滤波）(UAV height data fusion based on matlab)

一个100*100的正方形，用半径为10的圆覆盖，随机在100*100的平面内放置圆心，问至少需要多少个圆，才能使得成功覆盖整个区域的概率在95 以上？并给出一个均匀随机放置装置的分布图。(A 100* 100 square, a circle with a radius of 10 covers, 100* 100 randomly placed on the center plane, and asked how many circles at least, to cover the entire area so the probability of success of 95 or more? And gives a uniform distribution of randomly placed devices.)

这是一个用matlab做的跳频文件，希望能给大家带来一点灵感(it is the matlab to realize the hopping function)

MATLAB Coder™ generates standalone C and C++ code from MATLAB ® code. The generated source code is portable and readable. MATLAB Coder supports a subset of core MATLAB language features, including program control constructs, functions, and matrix operations. It can generate MEX functions that let you accelerate computationally intensive portions of MATLAB code and verify the behavior of the generated code.

开关磁阻电机斩波控制的simulink模型(current chopping control for switched reluctance drive)

In the computer science field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.[1] Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. Genetic algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics, pharmacometrics and other fields.

理想大气数据计算密度 压强 温度，0-40km以内高度(Ideal atmospheric pressure temperature data density within ,0-40km height)

Andrew NG的deep learning教程的练习补全(the completion of exercise or Andrew NG s deep learning tuturials)

遗传算法的电机优化程序，个人专门编写的，对于初学者来说，可以借鉴(function youhua1() 遗传算法 n-- 种群规模 ger-- 迭代次数 pc--- 交叉概率 pm-- 变异概率 v-- 初始种群（规模为n） f-- 目标函数值 fit-- 适应度向量 vx-- 最优适应度值向量 vmfit-- 平均适应度值向量 clear all close all clc 清屏 tic 计时器开始计时 n=20 ger=100 pc=0.9 pm=0.1 初始化参数 生成初始种群 v=init_population(n,44) 得到初始种群，22串长，生成20*22的0-1矩阵 [N,L]=size(v) 得到初始规模行，列 disp(sprintf( Number of generations: d ,ger)) disp(sprintf( Population size: d ,N)) disp(sprintf( Crossover probability: .3f ,pc)) disp(sprintf( Mutation probability: .3f ,pm)) sprintf可以控制输出格式 待优化问题)

ac and dc power transmissions. No alterations of conductors, insulator strings, and towers of the original line are needed