On continuous-time sparse identification of nonlinear polynomial systems
By: Mazen Alamir
Potential Business Impact:
Helps cars understand their own engine parts.
This paper leverages recent advances in high derivatives reconstruction from noisy-time series and sparse multivariate polynomial identification in order to improve the process of parsimoniously identifying, from a small amount of data, unknown Single-Input/Single-Output nonlinear dynamics of relative degree up to 4. The methodology is illustrated on the Electronic Throttle Controlled automotive system.
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