Linear and nonlinear properties of cochlear transduction—Application of a nonlinear system identification technique

1997 ◽  
Vol 102 (6) ◽  
pp. 3817-3817
Author(s):  
Geetha Krishnan
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Nonlinear Properties ◽  
Identification Technique ◽  
Linear And Nonlinear

Identification of Instantaneous Frequency and Damping From Transient Decay Data

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Mengshi Jin ◽  
Wei Chen ◽  
Matthew R. W. Brake ◽  
Hanwen Song
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Signal To Noise Ratio ◽  
Nonlinear System Identification ◽  
Uncertainty Assessment ◽  
Identification Procedure ◽  
Zero Crossing ◽  
Nonlinear Properties ◽  
Fitting Method ◽  
Comparison Results

Abstract Jointed interfaces, damage, wear, or non-idealized boundary conditions often introduce nonlinear characteristics to assembled structures. Consequently, extensive research has been carried out regarding nonlinear system identification. The development of nonlinear system identification is also enabling the intentional application of nonlinearities towards practical fields such as vibration control and energy harvesting. This research proposes a nonlinear identification procedure that consists of two steps: first, the raw data is filtered by the Double Reverse Multimodal Decomposition method that involves system reconstruction, expansion, and filtering twice. Second, the Peak Finding and Fitting method is applied to the filtered signal to extract the instantaneous amplitude and frequency. The identification procedure is applied to the measured responses from a jointed structure to assess its efficacy. The results are compared with those obtained from other well-known methods—the Hilbert transform and zero-crossing methods. The comparison results indicate that the Peaking Finding and Fitting method extracts the amplitude of the response signal more accurately. Consequently, this yields a higher signal-to-noise ratio in the extracted damping values. As a recommended last step, uncertainty assessment is conducted to calculate the 95% confidence intervals of the nonlinear properties of the system.

Linear and nonlinear system identification of autonomic heart-rate modulation

1997 ◽  
Vol 16 (5) ◽  
pp. 96-105 ◽  
Author(s):  
Ki.H. Chon ◽  
R. Mukkamala ◽  
K. Toska ◽  
T.J. Mullen ◽  
A.A. Armoundas ◽  
...  
Keyword(s):  
Heart Rate ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Rate Modulation ◽  
Linear And Nonlinear
Sign in / Sign up

Export Citation Format

Share Document