fangxiangtu

A*算法路径规划，有详细介绍文本 适合于初学者，学习算法。(A* algorithm for path planning, a detailed text is suitable for beginners, learning algorithms.)

example codes for pattern recongnition fourth edition

捷联惯导仿真,捷联惯导仿真,捷联惯导仿真(SINS simulation, the SINS simulation SINS simulation)

matlab代码关于ＡＲＭＡ时间序列模型(matlab code for ARMA time series model)

一篇非常优秀的论文：合成孔径雷达成像matlab仿真，附PDF和源码。 (Synthetic Aperture Radar Imaging Simulated in MATLAB（PDF and Matlab code）)

说明：  遗传算法（Genetic Algorithm，GA）最早是由美国的 John holland于20世纪70年代提出,该算法是根据大自然中生物体进化规律而设计提出的。是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法。该算法通过数学的方式,利用计算机仿真运算,将问题的求解过程转换成类似生物进化中的染色体基因的交叉、变异等过程。在求解较为复杂的组合优化问题时,相对一些常规的优化算法,通常能够较快地获得较好的优化结果。遗传算法已被人们广泛地应用于组合优化、机器学习、信号处理、自适应控制和人工生命等领域。(Genetic algorithm (GA) was first proposed by John Holland of the United States in the 1970s. It is designed and proposed according to the evolution law of organisms in nature. It is a computational model of biological evolution process simulating natural selection and genetic mechanism of Darwin's biological evolution theory. It is a method to search the optimal solution by simulating natural evolution process. The algorithm transforms the solving process of the problem into a process similar to the crossover and mutation of chromosome genes in biological evolution by means of mathematical method and computer simulation. When solving complex combinatorial optimization problems, compared with some conventional optimization algorithms, it usually can get better optimization results quickly. Genetic algorithm has been widely used in combinatorial optimization, machine learning, signal processing, adaptive control and artificial life.)

Face Detection In this program you will choose a photo and program will detect faces in this photo

显示当前系统时间 MATLAB GUI 界面(display current time and date with MATLAB GUI)

有关FM调制的实例，很有针对性，运行通过(FM modulation for instance, very targeted, run by)

说明：  粒子群算法阵列优化，对阵列方向图的综合，实现低副瓣阵列方向图(Particle swarm optimization (PSO) algorithm for array pattern synthesis to achieve low sidelobe array pattern)