Nonlinear Time-Series Prediction Using a Single MEMS Reservoir

Abstract

In this work, we show the computational potential of MEMS devices by predicting the dynamics of a 10th order nonlinear auto-regressive moving average (NARMA10) dynamical system. Modeling this system is considered complex due to its high nonlinearity and dependency on its previous values. To model the NARMA10 system, we used a reservoir computing scheme by utilizing one MEMS device as a reservoir, produced by the interaction of 100 virtual nodes. The virtual nodes are attained by sampling the input of the MEMS device and modulating this input using a random modulation mask. The interaction between virtual nodes within the system was produced through delayed feedback and temporal dependence. Using this approach, the MEMS device was capable of adequately capturing the NARMA10 response with a normalized root mean square error (NRMSE) = 6.18% and 6.43% for the training and testing sets, respectively. In practice, the MEMS device would be superior to simulated reservoirs due to its ability to perform this complex computing task in real time.