论文标题
基于ACO的机器人操纵器的自适应RBFN控制
ACO based Adaptive RBFN Control for Robot Manipulators
论文作者
论文摘要
本文介绍了一种使用基于蚂蚁菌落优化（ACO）的RBFN（径向基函数网络）近似操纵器的逆运动学的新方法。在本文中，使用ACO和LMS（最小平方）算法的训练解决方案在两阶段的训练程序中提出。为了解决一个问题，即K-均值聚类径向基函数（RBF）的群集结果很容易受到初始字符的选择，并收敛到RBF神经网络的局部最小值，蚂蚁菌落优化（ACO），以优化RBF神经网络中心，并减少隐藏层神经元NODES的数量。结果表明，对径向基函数（RBF）神经网络的蚂蚁集菌菌落优化的准确性较高，并且拟合程度已得到改善。
This paper describes a new approach for approximating the inverse kinematics of a manipulator using an Ant Colony Optimization (ACO) based RBFN (Radial Basis Function Network). In this paper, a training solution using the ACO and the LMS (Least Mean Square) algorithm is presented in a two-phase training procedure. To settle the problem that the cluster results of k-mean clustering Radial Basis Function (RBF) are easy to be influenced by the selection of initial characters and converge to a local minimum, Ant Colony Optimization (ACO) for the RBF neural networks which will optimize the center of RBF neural networks and reduce the number of the hidden layer neurons nodes is presented. The result demonstrates that the accuracy of Ant Colony Optimization for the Radial Basis Function (RBF) neural networks is higher, and the extent of fitting has been improved.
