利用残差改进深度学习算法(RIDLA)对非线性系统响应进行预测建模

IF 2.3 3区 工程技术 Q2 MECHANICS
Naijian Gu, Wenhua Wu, Kun Liu, Xinglin Guo
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引用次数: 0

摘要

预测随机激励下非线性系统的特定位置响应对于结构健康监测、优化设计和安全评估至关重要。由于数量和安装位置的限制,传统的基于传感器的响应测量面临挑战,而非线性时间历程分析则存在计算成本高和建模时间长的问题。工程中使用的简化回归方程往往缺乏准确性。本研究介绍了一种新颖的残差改进深度学习算法(RIDLA),用于为受到随机激励的非线性系统构建高精度预测模型。RIDLA 利用长短期记忆(LSTM)神经网络捕捉时间序列中的非线性关系,并通过与测量响应和计算残差进行交互式训练来迭代提高模型精度。这种方法能有效预测随机激励下非线性系统的时间历程响应。RIDLA 的性能通过预测两个典型非线性系统的响应得到了验证:一个是 6-DOF 非线性振荡器系统,另一个是受到随机激励的卫星-火箭连接的界面力。结果表明,RIDLA 可提供高度精确的预测,并可应用于其他复杂的非线性系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predictive modeling of nonlinear system responses using the Residual Improvement Deep Learning Algorithm (RIDLA)

Predictive modeling of nonlinear system responses using the Residual Improvement Deep Learning Algorithm (RIDLA)

Predicting specific location responses in nonlinear systems under random excitations is crucial for structural health monitoring, optimization design, and safety assessment. Traditional sensor-based response measurements face challenges due to limitations in quantity and installation positions, while nonlinear time history analysis suffers from high computational costs and modeling time. Simplified regression equations used in engineering often lack accuracy. This study introduces a novel Residual Improvement Deep Learning Algorithm (RIDLA) to construct high-precision prediction models for nonlinear systems subjected to random excitations. RIDLA leverages Long Short-Term Memory (LSTM) neural networks to capture nonlinear relationships in time series and iteratively improve model accuracy through interactive training with measured responses and computed residuals. This approach effectively predicts time history responses of nonlinear systems under random excitations. RIDLA’s performance is validated by predicting responses in two typical nonlinear systems: a 6-DOF nonlinear oscillator system and the interface force of a satellite–rocket connection subjected to random excitations. The results demonstrate that RIDLA provides highly accurate predictions and can be applied to other complex nonlinear systems.

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来源期刊
Acta Mechanica
Acta Mechanica 物理-力学
CiteScore
4.30
自引率
14.80%
发文量
292
审稿时长
6.9 months
期刊介绍: Since 1965, the international journal Acta Mechanica has been among the leading journals in the field of theoretical and applied mechanics. In addition to the classical fields such as elasticity, plasticity, vibrations, rigid body dynamics, hydrodynamics, and gasdynamics, it also gives special attention to recently developed areas such as non-Newtonian fluid dynamics, micro/nano mechanics, smart materials and structures, and issues at the interface of mechanics and materials. The journal further publishes papers in such related fields as rheology, thermodynamics, and electromagnetic interactions with fluids and solids. In addition, articles in applied mathematics dealing with significant mechanics problems are also welcome.
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