动态模糊RBF神经网络焊接接头力学性能预测建模

建立动态模糊径向基神经网络RBF（Radial Basis Function,RBF）焊接接头力学性能预测模型,克服静态RBF和模糊神经网络（Fuzzy Neural Network,FNN）在结构辨识、动态样本训练及学习算法的不足。该模型的结构参数不再提前预设,在训练过程中动态自适应调整,适用动态样本数据学习,学习算法引入分级学习和模糊规则修剪策略,加速训练并使模型结构更加紧凑。利用三种厚度、不同工艺TC4钛合金TIG焊接试验数据对该模型进行仿真。结果表明：模型具有较高的预测精度,适用于预测焊接接头力学性能,为焊接过程在线控制开辟了新的途径。...

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Bibliographic Details
Published in航空材料学报 Vol. 36; no. 5; pp. 26 - 30
Main Author 张永志 董俊慧 朱红玲
Format Journal Article
LanguageChinese
Published 内蒙古农业大学 机电工程学院,呼和浩特,010018%内蒙古工业大学 材料科学与工程学院,呼和浩特,010051%内蒙古大学 创业学院商学教学部,呼和浩特,010070 2016
Subjects
力学性能
动态模糊RBF神经网络
建模
焊接
预测
力学性能
welding
预测
焊接
prediction
mechanical property
dynamic fuzzy RBF neural network
建模
modelling
动态模糊RBF神经网络
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ISSN1005-5053
DOI10.11868/j.issn.1005-5053.2016.5.005

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Summary:建立动态模糊径向基神经网络RBF（Radial Basis Function,RBF）焊接接头力学性能预测模型,克服静态RBF和模糊神经网络（Fuzzy Neural Network,FNN）在结构辨识、动态样本训练及学习算法的不足。该模型的结构参数不再提前预设,在训练过程中动态自适应调整,适用动态样本数据学习,学习算法引入分级学习和模糊规则修剪策略,加速训练并使模型结构更加紧凑。利用三种厚度、不同工艺TC4钛合金TIG焊接试验数据对该模型进行仿真。结果表明：模型具有较高的预测精度,适用于预测焊接接头力学性能,为焊接过程在线控制开辟了新的途径。
Bibliography:A dynamic fuzzy RBF neural network model was built to predict the mechanical properties of welded joints, and the purpose of the model was to overcome the shortcomings of static neural networks including structural identification, dynamic sample training and learning algorithm. The structure and parameters of the model are no longer head of default, dynamic adaptive adjustment in the training, suitable for dynamic sample data for learning, learning algorithm introduces hierarchical learning and fuzzy rule pruning strategy, to accelerate the training speed of model and make the model more compact. Simulation of the model was carried out by using three kinds of thickness and different process TC4 titanium alloy TIG welding test data. The results show that the model has higher prediction accuracy, which is suitable for pre-dicting the mechanical properties of welded joints, and has opened up a new way for the on-line control of the welding process.
11-3159/V
ZHANG Yongzhi1 , DONG Junhui2, ZHU Hongling3（1. College
ISSN:1005-5053
DOI:10.11868/j.issn.1005-5053.2016.5.005