Application of improved back-propagation neural network to short fatigue crack evolution

被引：0
|
作者：
Wang, Zheng [1 ]
Liu, Jian-Xiong [1 ]
Wang, Lu [1 ]
Xie, Wei-Yun [1 ]
机构：
[1] School of Energy and Power Engineering, Dalian University of Technology, Dalian 116024, China
来源：
Cailiao Kexue yu Gongyi/Material Science and Technology | 2012年 / 20卷 / 06期
关键词：
Fatigue crack propagation - Torsional stress - Low-cycle fatigue - Backpropagation - Genetic algorithms - Neural networks;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
To research the complicated nonlinear dynamics process of the short crack evolution behavior, a way that improves back-propagation neural network aiming at evolution of short fatigue crack is shown in this paper. This method optimizes the weight of the BP network, and aggregates the characteristics of the local precise search of the BP network and the global optimization of the improved genetic algorithm, which integrates more factors and reflects complicated relation. Comparing the results of the experiment of short fatigue crack for low cycle under complex stress at high temperature with the simulation results of improved back-propagation neural network, it is proved that the method is feasible, accurate and converged quickly.
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页码：45 / 49
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