Time series analysis using recurrent neural networks based on dynamic mode decomposition with control: Operator analysis from observed records of small amplitude seismic motions

Abstract

Machine learning technology has developed rapidly in recent years, and has made great achievements in the field of civil engineering. Among them, Recurrent Neural Networks (RNNs) are highly effective in learning time-series data by storing the latent dynamic behavior of the system internally, and are expected to be applied to soil seismic response. On the other hand, such deep learning models lack interpretability, and there are issues in evaluating their reliability. In this study, we apply operator interpretation based on Dynamic Mode Decomposition with Control (DMDc) to RNNs, decompose and visualize the operators obtained by expanding the RNN model into modes, and use the operators generated by DMDc for learning, aiming for RNNs to acquire modes with better properties. The results of the study show that the goal of improving interpretability can be achieved while maintaining the same level of prediction performance compared to the case where DMDc is not considered.