A novel nonlinear system identification technique for modeling cochlear transduction using otoacoustic emissions data

1996 ◽  
Vol 100 (4) ◽  
pp. 2628-2628
Author(s):  
Geetha Krishnan ◽  
Mark E. Chertoff
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Otoacoustic Emissions ◽  
Nonlinear System Identification ◽  
Identification Technique

Nonlinear Model for Sub- and Superharmonic Motions of a MDOF Moored Structure, Part 2—Sensitivity Analysis and Comparison

2005 ◽  
Vol 127 (4) ◽  
pp. 291-299
Author(s):  
S. C. S. Yim ◽  
S. Raman ◽  
P. A. Palo
Keyword(s):  
Sensitivity Analysis ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Detailed Comparison ◽  
Identification Procedure ◽  
System Parameters ◽  
Nonlinear Responses ◽  
Identification Technique ◽  
Single Set

The nonlinear R-MI/SO system identification procedure and the parameters of the MDOF system identified in Part 1 are examined in detail in this paper. A parametric study is conducted and the results are presented on the sensitivity of the system parameters for two key nonlinear responses—subharmonic and superharmonic resonances. The parameters are compared to determine the appropriateness of using a single set of system parameters for both response regions. A detailed comparison of the MDOF and the corresponding SDOF system results is performed. The knowledge gained from the SDOF and MDOF studies on the applicability of the R-MISO technique for the system identification of MDOF submerged moored structures is discussed. The results show that the MDOF extension of the R-MI/SO nonlinear system identification technique works well; the resulting system parameters are relatively constant and can be applied to the both the sub- and superharmonic regions.

scholarly journals Comparing system identification techniques for identifying human-like walking controllers

2021 ◽  
Vol 8 (12) ◽  
Author(s):  
Dave Schmitthenner ◽  
Anne E. Martin
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Linear System ◽  
Nonlinear System Identification ◽  
Ordinary Least Squares ◽  
Human Gait ◽  
Slip Model ◽  
Identification Technique ◽  
Experimental Values ◽  
Identification Techniques

While human walking has been well studied, the exact controller is unknown. This paper used human experimental walking data and system identification techniques to infer a human-like controller for a spring-loaded inverted pendulum (SLIP) model. Because the best system identification technique is unknown, three methods were used and compared. First, a linear system was found using ordinary least squares. A second linear system was found that both encoded the linearized SLIP model and matched the first linear system as closely as possible. A third nonlinear system used sparse identification of nonlinear dynamics (SINDY). When directly mapping states from the start to the end of a step, all three methods were accurate, with errors below 10% of the mean experimental values in most cases. When using the controllers in simulation, the errors were significantly higher but remained below 10% for all but one state. Thus, all three system identification methods generated accurate system models. Somewhat surprisingly, the linearized system was the most accurate, followed closely by SINDY. This suggests that nonlinear system identification techniques are not needed when finding a discrete human gait controller, at least for unperturbed walking. It may also suggest that human control of normal, unperturbed walking is approximately linear.

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