| 摘 要: 将形状记忆合金混编三维织物驱动器(SMA驱动器)植入复合材料板簧中,可以使大型板式结构具有变刚度潜力。为提高驱动器空间布置参数的寻优效率,文章基于BP神经网络预测不同驱动器布置参数下的板簧刚强度,并采用遗传算法提升预测精度。结果表明,经过遗传算法(Genetic Algorithm,GA)优化后的BP神经网络(Back Propagation Neural Networks)模型对板簧刚强度性能的平均预测精度为96.9%,优于传统BP神经网络的平均预测精度(92.7%),并且寻优效率比传统智能算法提升了172倍。该研究为大型板式结构的空间布置参数寻优算法提供了有益参考。 |
| 关键词: SMA驱动器 空间布置参数 BP神经网络 遗传算法 寻优效率 |
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中图分类号: TP391
文献标识码: A
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| 基金项目: 国家自然科学基金(52102430) |
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| Research on Stiffness and Strength Prediction of Variable Stiffness Composite Leaf Spring Based on GA-Optimized Neural Network |
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YANG Yinze, KE Jun
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(College of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China)
737848170@qq.com; jlukejun@163.com
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| Abstract: Embedding Shape Memory Alloy composite 3D fabric actuators (SMA actuators) into composite leaf spring can endow large plate-like structures with variable stiffness potential. To enhance the optimization efficiency of space layout parameters of actuators, this study uses a BP (Back Propagation) neural network to predict the leaf spring strength under different actuator layout parameters and employs Genetic Algorithm ( GA) to improve prediction accuracy. Results show that the BPNN (Back Propagation Neural Network) model optimized by GA achieves an average prediction accuracy of 96. 9% for leaf spring strength performance, outperforming the average prediction accuracy of traditional BPNN (92.7% ), and enhancing optimization efficiency by 172 times compared to traditional intelligent algorithms. This research provides valuable insights for optimizing the space layout parameters of large plate-like structures. |
| Keywords: SMA actuators space layout parameters BP Neural Networks (BPNN) GA optimization efficiency |
