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超弹性形状记忆合金循环特性的BP神经网络模型研究
发表时间：2011-07-31 浏览量：1548 下载量：428
| 全部作者： | 任文杰，贾茹，韩雷 |
| 作者单位： | 河北工业大学土木工程学院 |
| 摘 要： | 对超弹性形状记忆合金（shape memory alloy，SMA）丝进行循环加卸载试验，在此试验基础上，采用误差反向传播（back-propagation，BP）神经网络原理，建立超弹性SMA的循环本构模型。该模型的输入为循环次数、加卸载信息和应变值，输出为应力值。从试验曲线中选择25条用于训练网络，5条用于检验网络。计算证明了该模型具有很高的预测精度。这为超弹性SMA循环本构模型的建立提供了一个新的思路。 |
| 关 键 词： | 防灾减灾工程及防护工程；形状记忆合金；超弹性；倒传递类神经网络；本构模型 |
| Title： | Study of BP neural network model of cyclic behavior of super-elastic shape memory alloy |
| Author： | REN Wenjie, JIA Ru, HAN Lei |
| Organization： | School of Civil Engineering, Hebei University of Technology |
| Abstract： | In this study, the cyclic tensile tests were carried out on the super-elastic shape memory alloy (SMA) wires. According to the testing data, a constitutive model for the cyclic behavior of super�elastic SMA was put forward based on back�propagation (BP) neural network. In this model, the inputs included the number of loading cycles, the index of loading and unloading and the strain; and the output was the stress. 25 experimental curves were used for training the neural network and 5 curves for testing the neural network. Simulations indicated that the model had a high accuracy of predictions. The model ran simply, and the training and test data can be acquired easily. This study provides a new method for developing the cyclic constitutive model of super-elastic SMA. |
| Key words： | disaster prevention and reduction engineering; shape memory alloy; super-elasticity; back-propagation neural network; constitutive model |
| 发表期数： | 2011年7月第14期 |
| 引用格式： | 任文杰，贾茹，韩雷. 超弹性形状记忆合金循环特性的BP神经网络模型研究[J]. 中国科技论文在线精品论文，2011，4（14）：1283-1288. |
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