A Least Squares Based Online Parameter Estimation Algorithm for Artificial Hair Cell Model

This study focuses on identification of the model parameters of an artificial cochlea. For this purpose, a least squares estimator incorporated with a filtering approach, is proposed to accurately determine the parameters of the artificial hair cell model. The proposed approach leverages readily available standard signals from the cell, thereby eliminating the need for direct acceleration measurements. Utilization of the proposed method enables efficient and practical parameter identification. To validate the effectiveness of the proposed approach, numerical simulations are provided to illustrate the successful identification of uncertain model parameters.