Black-box models for Bacterial-Cellulose-based sensors

In this work, black-box modeling techniques are applied to Bacterial Cellulose-based sensors to characterize their dynamic behavior. Several classes of linear and nonlinear models, including Finite Impulse Response, AutoRegressive with eXogenous Input, Nonlinear Finite Impulse Response, Nonlinear AutoRegressive with eXogenous Input, and Long Short- Term Memory networks, are developed and compared. The performance of each model is evaluated based on a one-stepahead prediction and a∞-step-ahead simulation using standard performance metrics such as Root Mean Square Error, Mean Absolute Error and the coefficient of determination. The results show the strengths and limitations of the different modeling approaches in capturing the dynamics of BC-based transducers. The ARX model showed the best results for the prediction in one step, but poor results were obtained when the prediction was considered in ∞ steps. The NFIR model is instead the best choice for long-term prediction.