采用BP神经网络模型预测改性骨胶的初始黏度  

Initial Viscosity Prediction of Bone Glue with BP Neural Network Model

作　　者：邱辉[1] 李仲谨[1] 郁磊[2] 朱雷[1] 余丽丽[1] 赵阳[1] 

机构地区：[1]教育部轻化工助剂化学与技术重点实验室陕西科技大学,陕西西安710021 [2]中国矿业大学信息与电气工程学院,江苏徐州221008

出　　处：《中国皮革》2009年第23期21-24,共4页China Leather

基　　金：陕西省星火计划(2004kx3-10);陕西科技大学研究生创新基金资助

摘　　要：建立了一个基于L-M(Levenberg-Marquardt)优化算法的BP神经网络预测模型,用于骨胶初始黏度的预测。对涉及骨胶接枝改性的几个主要因素,如氢氧化钠用量、碱解时间、接枝共聚温度、环氧氯丙烷用量和接枝共聚时间等为考察对象,以初始黏度为指标,对16个试验样本进行了训练建模,并对2个测试样本进行了预测。结果表明:骨胶初始黏度预测值和试验值符合良好,相对误差小于2%。该方法可行有效,为快捷、经济地开发研制新的胶粘剂提供了新的思路和有效手段。A model using L - glue. Some factors about of alkaline hydrolysis, th grafting M optimization algorithm BP neural network was established to predict the initial viscosity of bone of bone glue were studied such as the amounts of sodium hydrate and epichlorohydrin, the time e polymerization temperature. The initial viscosity was used as inspecting marker, the model was estab- lished on training the 16 samples, and 2 samples were used as prediction. The resuhs show that the predicted dates are in good agreement with the experimental dates, with the relative error being less than 2%. This system has friendly interfaces, extensive application, good operating feasibility and reliability examined with the present of adhesive.

关 键 词：L-M优化算法 骨胶 初始黏度 预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TQ431.5[自动化与计算机技术—控制科学与工程]