论文标题
基于金字塔神经网络的滞后行为模拟：原理，网络体系结构，案例研究和解释
Hysteretic Behavior Simulation Based on Pyramid Neural Network:Principle, Network Architecture, Case Study and Explanation
论文作者
论文摘要
对材料和组件的滞后行为进行准确有效的模拟对于结构分析至关重要。基于神经网络的替代模型在平衡效率和准确性方面显示出巨大的潜力。但是，基于单层功能的序列信息流和预测会对网络性能产生不利影响。因此，本文提出了加权堆叠的金字塔神经网络结构。该网络通过引入多级快捷方式来建立金字塔体系结构，以直接在输出模块中集成功能。此外，提出了加权堆叠策略来增强常规特征融合方法。随后，将重新设计的体系结构与其他常用的网络体系结构进行了比较。结果表明，重新设计的体系结构在87.5％的情况下优于替代方案。同时，通过专门设计的实验分析了不同基本网络体系结构的长期和短期记忆能力，该实验可以为网络选择提供宝贵的建议。
An accurate and efficient simulation of the hysteretic behavior of materials and components is essential for structural analysis. The surrogate model based on neural networks shows significant potential in balancing efficiency and accuracy. However, its serial information flow and prediction based on single-level features adversely affect the network performance. Therefore, a weighted stacked pyramid neural network architecture is proposed herein. This network establishes a pyramid architecture by introducing multi-level shortcuts to integrate features directly in the output module. In addition, a weighted stacked strategy is proposed to enhance the conventional feature fusion method. Subsequently, the redesigned architectures are compared with other commonly used network architectures. Results show that the redesigned architectures outperform the alternatives in 87.5% of cases. Meanwhile, the long and short-term memory abilities of different basic network architectures are analyzed through a specially designed experiment, which could provide valuable suggestions for network selection.
