基于PCA和改进的PSO-SVM的机器人移动方向分类

随着人们创新水平的不断提高,为了更加准确的实现机器人的导航任务,提出了一种基于改进的粒子群优化支持向量机中的参数的方法.首先利用主成分分析法对数据进行降维,然后利用改进的粒子群优化算法,对SVM中的惩罚参数c和核函数的参数g进行优化,最后代入到SVM中,以此来达到运用SVM对机器人的导航任务进行分类识别.相对于其他算法,容易发现改进的粒子群优化算法优化后的支持向量机可以达到很好的效果.这种识别分类可以帮助人们很好的对机器人进行导航,对今后机器人的研究具有很大的应用价值.

Classification of Robot Moving Direction Based on PCA and Improved PSO-SVM

With the improvement of the level of innovation,we propose a method based on improved particle swarm optimization to optimize the parameters of the support vector machine.in order to achieve more accurate navigation of the robot.Firstly,we can use the principal component analysis to reduce the dimension of the data and use improved particle swarm optimization algorithm,which can optimize penalty parameter c of SVM and parameter g of kernel function,then substituted into the SVM,in order to achieve classification and recognition using SVM for robot navigation task.Compared with other algorithms,it is easy to find that the support vector machine can achieve very good results after optimization of the improved particle swarm optimization algorithm.This classification can help people to navigate the robot very well,this research of the robot has great application value in the future.