基于改进PSO-BP网络的钻削刀具参数选择的研究  

Systematic Research of Drilling Parameter Preferences Based on Hybrid Algorithm of PSO and BP

作　　者：姚明明[1] 王培东[1] 周洪玉[1] 

机构地区：[1]哈尔滨理工大学计算机科学与技术学院,黑龙江哈尔滨150080

出　　处：《哈尔滨理工大学学报》2008年第5期57-60,共4页Journal of Harbin University of Science and Technology

基　　金：黑龙江省"十五"科技攻关项目(GA02A401-6)

摘　　要：提出了用非线性惯性因子ω改进的微粒群算法与BP神经网络相结合的方法,适当选择钻削刀具的切削用量,克服了BP网络训练时间长,因易陷入局部最优点而不利于全局最优点搜索的不足.通过相同的实验样本测试发现,与以前的BP和GA-BP算法相比,训练时间分别缩短了73s和21s,测试的正确率分别提高了0.83%和0.32%.This paper proposes combining BP Network and an improved Particle Swarm Optimizer with nonlinear inertia factor, to choose drilling tool＇ s cutting dosage. This method resolves the BP Network＇ s long training time and difficulty to search the global minimum since plunging into local minimum. From the same experimental sample, compared with previous BP arithmetic and GA-BP arithmetic ,the training time with this algorithm has been shortened 73s and 52s respectively, meanwhile the accurate rate has been improved O. 83% and 0. 32% respectively.

关 键 词：微粒群 BP神经网络 切削用量 钻削刀具 

分 类 号：TG713[金属学及工艺—刀具与模具]