Ensemble of Multi-time Resolution Recurrent Neural Networks for Enhanced Feature Extraction in High-Rate Time Series
Ensemble of Multi-time Resolution Recurrent Neural Networks for Enhanced Feature Extraction in High-Rate Time Series
复制标题
用于增强高速时间序列特征提取的多时间分辨率循环神经网络集合
DOI：
10.1007/978-3-030-77135-5_24
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Dodson, J.
中科院分区：
文献类型：
--
作者：
Barzegar, V.;Laflamme, S.;Hu, C.;Dodson, J.
经历高速率动态事件的系统称为高速率系统，通常在不到10毫秒的时间内经历幅度高于100克的加速度。例子包括自适应安全气囊部署系统、高超声速飞行器和主动爆炸缓解系统。考虑到这类系统的关键功能，为了确保目标性能，需要准确和快速的建模工具。然而，这些系统的独特特性包括(1)外部负载的大不确定性，(2)高度的非平稳性和严重的扰动，以及(3)由于系统配置的变化而产生的未建模动态，以及快速变化的环境，限制了物理建模工具的适用性。在本章中，提出了一种基于神经网络的方法来对高速率系统进行建模和预测。它由递归神经网络(RNN)和同时训练的短序列长短期记忆(LSTM)细胞组成。为了支持多步超前预测，每个RNN的输入空间是使用主成分分析单独选择的，主成分分析提取动态的不同分辨率。在高速系统的实验数据上验证了该算法的有效性。结果表明，与启发式方法相比，该算法在构建输入空间时显著提高了超前预测的质量。
Systems experiencing high-rate dynamic events, termed high-rate systems, typically undergo accelerations of amplitudes higher than 100 g in less than 10 ms. Examples include adaptive airbag deployment systems, hypersonic vehicles, and active blast mitigation systems. Given the critical functions of such systems, accurate and fast modeling tools are necessary for ensuring the target performance. However, the unique characteristics of these systems, which consist of (1) large uncertainties in the external loads, (2) high levels of nonstationarities and heavy disturbances, and (3) unmodeled dynamics generated from changes in system configurations, in combination with the fast-changing environment, limit the applicability of physical modeling tools. In this chapter, a neural network-based approach is proposed to model and predict high-rate systems. It consists of an ensemble of recurrent neural networks (RNNs) with short-sequence long short-term memory (LSTM) cells which are concurrently trained. To empower multi-step-ahead predictions, the input space for each RNN is selected individually using principal component analysis that extracts different resolutions on the dynamics. The proposed algorithm is validated on experimental data obtained from a high-rate system. Results showed that this algorithm significantly improves the quality of step-ahead predictions over a heuristic approach in constructing the input spaces.
DOI：
10.3390/s21061954
发表时间：
2021-03-10
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Barzegar V;Laflamme S;Hu C;Dodson J
通讯作者：
Dodson J
影响因子：
8.4
作者：
Downey, Austin;Hong, Jonathan;Scheppegrell, James
通讯作者：
Scheppegrell, James