Abstract:This paper deals with the identification of stable linear systems from input−output response
data with low signal/noise ratios. Specifically we focus on nonparametric (finite impulse or step
response, FIR or FSR) models widely used in model predictive control. A polynomial kernel
representation is proposed to reduce the number of parameters needed to represent the model.
This leads to parsimonious yet robust models. Linear least-squares estimation can be used with
these polynomial models. The time delay and re… Show more
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