[1]黄俊,徐越兰.碳钢焊条熔敷金属力学性能非线性神经网络组合预测[J].南京理工大学学报(自然科学版),2012,36(05):800.
 HUANG Jun,XU Yue-lan.Nonlinear Combination Prediction of Mechanical Properties of CarbonSteel Electrode Deposited Metal Based on Neural Network[J].Journal of Nanjing University of Science and Technology,2012,36(05):800.
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碳钢焊条熔敷金属力学性能非线性神经网络组合预测
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
36卷
期数:
2012年05期
页码:
800
栏目:
出版日期:
2012-10-31

文章信息/Info

Title:
Nonlinear Combination Prediction of Mechanical Properties of CarbonSteel Electrode Deposited Metal Based on Neural Network
作者:
黄俊徐越兰
南京理工大学材料科学与工程学院,江苏南京210094
Author(s):
HUANG JunXU Yue-lan
School of Materials Science and Engineering,NUST,Nanjing 210094,China
关键词:
遗传算法神经网络碳钢焊条熔敷金属非线性组合预测
Keywords:
genetic algorithm neural network carbon electrodes deposited metals nonlinearcombination prediction
分类号:
TG115. 28
摘要:
为了提高碳钢焊条熔敷金属延伸率和冲击功力学性能指标的预测准确性,建立了基于反向传播(BP)神经网络、径向基函数(RBF) 神经网络、自适应模糊神经网络( AFNN)3 种单一模型的碳钢焊条熔敷金属力学性能非线性神经网络组合预测模型。综合运用遗传算法优化BP神经网络连接权的方法对模型预测性能进行了有效改进。利用试验获得的55 组相关样本数据对模型进行训练和验证。结果表明,延伸率、冲击功指标的预测平均相对误差分别降为3. 15%和2. 67%,远小于5%,满足实际生产要求;与采用单一预测模型相比,使用基于遗传算法的神经网络组合预测模型能够显著提高预测准确性和泛化能力。
Abstract:
To improve the prediction accuracy of the elongation percentage and the impacting work ofthe carbon steel electrode deposited metal,a nonlinear combination predicting neural network modelis build based on predicted data acquired from the single predicting models of back propagation(BP),radial basis fundion(RBF) and adaptive fuzzy neural network(AINN). By using the geneticalgorithm to optimize the connection weight of BP network,the model predicting property is improvedeffectively. Fifty-five samples are selected from experiments to train and verify the model. Resultsshow that predicted average relative errors of the elongation percentage and the impacting workdecrease to 3. 15% and 2. 67% separately,both of them are far below 5% and satisfy the practicaldemands. Compared with the single predicting model,the accuracy and generalization ability of theneural network combination model based on the genetic algorithm(GA)are greatly improved.

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备注/Memo

备注/Memo:
收稿日期:2010-07-22修回日期:2012-08-17作者简介:黄俊(1978-),女,博士,讲师,主要研究方向:气动及机电一体化技术、焊接材料智能化设计、焊接过程数值模拟,E-mail:huangjun0061@126. com。
更新日期/Last Update: 2012-11-26