Imperialistic-Competitive-Algorithm-(ICA)-IEEE-CE

基于视觉的PUMA560机械臂的抓取路径规划问题，包括图像采集与处理，机械臂建模，三维重建，视觉伺服控制等内容(Based the visual PUMA560 manipulator the crawl path planning issues, including the contents of the image acquisition and processing, robotic arm modeling, three-dimensional reconstruction, visual servo control)

采用DC加权的恒定束宽的宽带波束形成器，其中迭代用的是Newton迭代法(DC weighted using constant beamwidth broadband beamformer, which iteration is the Newton iterative method)

Underdetermined Blind Source Separation

184号规则下的交通流仿真模型，184号规则下的交通流仿真模型(Traffic Flow Simulation Model No. 184 under the rules)

说明：  sar雷达成像的后向投影算法的matlab代码(Matlab code of backward projection algorithm for SAR radar imaging)

有关粒子群算法的源程序，用matlab语言编得，可用于求解函数优化问题(Particle Swarm Optimization on the source, using matlab language was made, can be used to solve the function optimization problem)

用Canny算子检测图像的边缘 P0404：图像的阈值分割 P0405：用水线阈值法分割图像 P0406：对矩阵进行四叉树分解 P0407：将图像分为文字和非文字的两个类别 P0408：形态学梯度检测二值图像的边缘 P0409：形态学实例——从PCB图像中删除所有电流线，仅保留芯片对象(with Canny operator to detect the edges in the image P0404 : image thresholding segmentation P0405 : water line threshold method image segmentation P0406 : matrix Quadtree P0407 : images into text and non-text of the two categories P0408 : morphological gradient detection Binary Image Edge P0409 : morphology example-- Images from the PCB to delete all current lines, retaining only chip targets)

关于matlab的基础语言和其他算法，有利于初学者(Based on the matlab language and other algorithms, help beginners)

本程序是有限时域差分源程序，有详细注解，方便学习理解。(This program is a limited time domain difference source program, has detailed notes, easy to learn to understand. )

粒子群优化算法（PSO）是一种基于种群的随机优化技术，由Eberhart和Kennedy于1995年提出。粒子群算法模仿昆虫、兽群、鸟群和鱼群等的群集行为，这些群体按照一种合作的方式寻找食物，群体中的每个成员通过学习它自身的经验和其他成员的经验来不断改变其搜索模式。 (Particle swarm optimization algorithm (PSO) is a population based stochastic optimization technique, put forward by Eberhart and Kennedy in 1995. Particle swarm optimization (pso) algorithm on insects, herds, flocks and schools of fish swarm behavior, and these groups to find food in a cooperative way, each member of the group itself by learning experience and the experience of the other members to continuously change its search pattern.)