一种基于Hopfield算法的螺丝拧装机路径优化方法  

Screw driver path optimization method based on Hopfield algorithm

作　　者：杨政 郑晟 YANG Zheng;ZHENG Sheng(College of Electrical and Power Engineering,Taiyuan University of Technology,Taiyuan 030024,China)

机构地区：[1]太原理工大学电气与动力工程学院,山西太原030024

出　　处：《现代电子技术》2021年第19期158-162,共5页Modern Electronics Technique

基　　金：山西省面上青年基金项目(201901D211079)。

摘　　要：为了提升拧装机的加工效率,将装配刀头的运行轨迹规划问题转化为旅行商问题(TSP),应用连续型Hopfield神经网络算法对工作节点进行优化排序,并且以16节点的控制柜安装板为分析案例,得出了刀头拧装的执行路径。此外,针对传统Hopfield算法在实际运行过程中容易产生无效路径以及陷入局部最优的不足,融入蚁群算法的思想研究了一种改进型Hopfield算法,在一定范围内自动调整神经元之间的连接权值,辅助网络产生期望的能量极小点。仿真结果表明,改进算法降低了产生无效路径的几率,有效提升了寻优性能。In order to improve the processing efficiency of the screw driver,the running path planning problem of the assembly cutter head is transformed into the traveling salesman problem(TSP),the continuous Hopfield neural network algorithm is applied to optimize the sequencing of working nodes,and the 16⁃node control cabinet mounting plate is taken as a case,so as to get the execution path of the screwing of the cutter head.In addition,in view of the fact that the traditional Hopfield algorithm is prone to generating invalid path and falling into local optimum in the actual operation process,an improved Hopfield algorithm is studied by integrating the idea of ant colony algorithm,which can automatically adjust the connection weight between neurons in a certain range,and assist the network to generate the desired energy minimum.The simulation results show that the improved algorithm can reduce the probability of invalid path generation and improve the optimization performance.

关 键 词：螺丝拧装机 路径优化 HOPFIELD神经网络 能量函数 TSP 蚁群算法 节点排序 连接权值 

分 类 号：TN911.1-34[电子电信—通信与信息系统] TP181[电子电信—信息与通信工程]