TY - JOUR
T1 - Adaptive Filtering Approach with Forgetting Factor for Stochastic Signals Applied to EEG
AU - Aguilar-Cruz, Karen Alicia
AU - De Jesus Medel-Juarez, Jose
AU - Zagaceta-Alvarez, Maria Teresa
AU - Palma-Orozco, Rosaura
AU - Urbieta-Parrazales, Romeo
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - This paper presents a new stochastic adaptive estimation-identification technique for nonstationary systems. The proposed method enhances the initial results from an on average estimation, and its identification, through a generalized adaptable function based on the Exponential Forgetting Factor (EFF), and the Sliding Mode (SM) regarding the error identification. In this form, the presented process includes the function implementation in three stages-estimation, adaptive estimation, and adaptive estimation-identification, allowing us to observe the gradual convergence to a nonstationary reference signal. Simulations first introduce convergence level checks obtained from the estimation and identification of artificial signals. After that, the algorithm is applied for real references, considering the Electroencephalogram (EEG) signals taken from a public database, finding their internal nonstationary gains, indirectly. Finally, the results include a performance comparison between the proposed strategy concerning the Recursive Least Square (RLS), the Least Mean Square (LMS), and the Kalman Filter (KF).
AB - This paper presents a new stochastic adaptive estimation-identification technique for nonstationary systems. The proposed method enhances the initial results from an on average estimation, and its identification, through a generalized adaptable function based on the Exponential Forgetting Factor (EFF), and the Sliding Mode (SM) regarding the error identification. In this form, the presented process includes the function implementation in three stages-estimation, adaptive estimation, and adaptive estimation-identification, allowing us to observe the gradual convergence to a nonstationary reference signal. Simulations first introduce convergence level checks obtained from the estimation and identification of artificial signals. After that, the algorithm is applied for real references, considering the Electroencephalogram (EEG) signals taken from a public database, finding their internal nonstationary gains, indirectly. Finally, the results include a performance comparison between the proposed strategy concerning the Recursive Least Square (RLS), the Least Mean Square (LMS), and the Kalman Filter (KF).
KW - Adaptive estimation
KW - Electroencephalogram
KW - Parameter estimation
KW - Signal processing algorithm
KW - System identification
UR - http://www.scopus.com/inward/record.url?scp=85087064120&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.2997850
DO - 10.1109/ACCESS.2020.2997850
M3 - Artículo
SN - 2169-3536
VL - 8
SP - 101274
EP - 101283
JO - IEEE Access
JF - IEEE Access
M1 - 9108253
ER -