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Multi-Time Resolution Ensemble LSTMs for Enhanced Feature Extraction in High-Rate Time Series

机译：用于增强功能提取的多次分辨率集合LSTMS在高速时间序列中提取

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摘要

Systems experiencing high-rate dynamic events, termed high-rate systems, typically undergo accelerations of amplitudes higher than 100 g-force in less than 10 ms. Examples include adaptive airbag deployment systems, hypersonic vehicles, and active blast mitigation systems. Given their critical functions, 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 non-stationarities and heavy disturbances, and (3) unmodeled dynamics generated from changes in system configurations, in combination with the fast-changing environments, limit the applicability of physical modeling tools. In this paper, a deep learning algorithm is used to model high-rate systems and predict their response measurements. It consists of an ensemble of short-sequence long short-term memory (LSTM) cells which are concurrently trained. To empower multi-step ahead predictions, a multi-rate sampler is designed to individually select the input space of each LSTM cell based on local dynamics extracted using the embedding theorem. The proposed algorithm is validated on experimental data obtained from a high-rate system. Results showed that the use of the multi-rate sampler yields better feature extraction from non-stationary time series compared with a more heuristic method, resulting in significant improvement in step ahead prediction accuracy and horizon. The lean and efficient architecture of the algorithm results in an average computing time of 25 μs, which is below the maximum prediction horizon, therefore demonstrating the algorithm’s promise in real-time high-rate applications.
机译：体验高速动态事件的系统，称为高速系统，通常在少于10毫秒的幅度高于100g力的幅度的加速度。示例包括自适应安全气囊展署系统，超音速车辆和主动爆破缓解系统。鉴于其关键功能，准确和快速的建模工具是确保目标性能所必需的。然而，这些系统的独特特征，包括（1）外部负载中的大不确定性，（2）高水平的非平等性和重度扰动，以及（3）组合中系统配置中的变化产生的未拼质动态使用快速更改的环境，限制了物理建模工具的适用性。在本文中，深入学习算法用于建模高速速率系统并预测其响应测量。它由与培训的短序长短期内存（LSTM）单元的集合组成。为了使多步前方预测推导，多速率采样器旨在基于使用嵌入定理提取的本地动态来单独选择每个LSTM单元的输入空间。该算法在从高速系统获得的实验数据上验证。结果表明，使用多速率采样器的使用产生了与更启发式的方法相比的非静止时间序列的更好的特征提取，从而提高了前方预测精度和地平线的显着改善。算法的瘦且有效的架构导致平均计算时间为25μs，其低于最大预测地平线，因此在实时高速速率应用中展示了算法的承诺。

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