中山大学学报（自然科学版）2018，Vol.57Issue(2)：116-122,7.DOI:10.13471/j.cnki.acta.snus.2018.02.016
基于改进PSO神经网络的微米木纤维切削参数优化
Optimization study of cutting parameters of micron wood fiber based on improved PSO neural network
摘要
Abstract
In order to improve the process of micron wood fiber cutting,an improved particle swarm algo-rithm and BP neural network based on the combination of optimization algorithm is proposed to achieve the precision machining of micron wood fiber.The error back propagation algorithm is used to achieve the best structure selection of the complex relationship between cutting parameters.The improved particle swarm optimization algorithm(PSO)solves the defect of local minimum convergence of BP network,and gives a scientific and reasonable output of cutting parameters.The precision and effectiveness of the pre-cision training of the algorithm are verified by the simulation and optimization experiments of the cutting parameters of different tree species.The research shows that the improved optimization algorithm proposed in this paper can predict the cutting parameters of the wood to be processed and has a high training preci-sion.关键词
木纤维切削/切削参数优化/BP神经网络/粒子群优化算法Key words
wood fiber cutting/cutting parameter optimization/BP neural network/particle swarm op-timization algorithm分类
交通工程引用本文复制引用
齐红,任洪娥,贾鹤鸣,袁世庆..基于改进PSO神经网络的微米木纤维切削参数优化[J].中山大学学报（自然科学版）,2018,57(2):116-122,7.基金项目
国家自然科学基金(31370566) （31370566）
黑龙江省自然科学基金重点项目(ZD201203) （ZD201203）
黑龙江省研究生教育创新工程项目(JGXM-HLJ-2016014) （JGXM-HLJ-2016014）
