非线性回归的尖端神经网络.
Alexander Henkes1,2, Jason K Eshraghian3, Henning Wessels2
1Computational Mechanics Group, ETH Zurich, Zurich, Switzerland.
Royal Society open science
|May 3, 2024
概括
尖端神经网络 (SNN) 为工程任务 (如结构健康监测) 提供了显著的能量和内存节省. 这项研究引入了新的SNN架构,用于准确和高效的回归,将列车解码为实数.
科学领域:
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
- 材料科学 材料科学 材料科学
背景情况:
- 尖端神经网络 (SNN) 是第三代神经网络,与传统网络相比,它有望减少能源和内存的消耗.
- 他们的大脑启发的时间和神经元稀疏性是神经形态硬件的理想选择.
- 能源效率对工程应用至关重要,特别是在数据驱动力学和结构健康监测方面.
研究的目的:
- 提出SNNs在回归任务中的精度和能源效率的新配方.
- 引入一个网络拓,用膜潜力将二进制尖峰列车解码为实数.
- 导出各种SNN架构,从前到长期短期存储器 (LSTM) 网络,优化能源效率.
主要方法:
- 开发了一种新的网络拓,用于将尖峰列车解码为实数.
- 制定并衍生了几种SNN架构,包括尖端的前和尖端的LSTM网络.
- 保证的架构避免密集的层,以最大限度地提高SNN的能源效率.
主要成果:
- 通过涉及各种材料模型的数值示例证明了拟议的SNN架构的准确性.
- 在线性,非线性和历史依赖的材料模型上验证的性能.
- 在保持SNN固有的能源效率的同时,实现了高精度.
结论:
- 拟议的SNN架构为工程中的回归任务提供了准确和高能效的解决方案.
- 新的解码方法和无密层架构释放了SNN对神经形态硬件的全部潜力.
- 该框架可适应在表述的机械示例之外的定制函数回归.
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