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The model used in the NMPC is a nonlinear, additive, autoregressive model with exogenous (NAARX) inputs.
The procedure proposed here associates an ANN with a Nonlinear AutoRegressive model with eXogenous inputs (NARX).
The output of this LLM algorithm is an autoregressive model with exogenous input (ARX).
The SVR nonlinear autoregressive model with exogenous inputs (NARX) was developed and tested using experimental data collected during fourteen years.
With such an assumption, auto-regressive models with exogenous variables (ARX) may fit well to the data.
A moving-average model with exogenous input (MAX) is given to describe the transmission channel and colored noise.
This paper presents a non-linear moving average model with exogenous inputs (NMAX) and a non-linear auto-regressive moving average model with exogenous inputs (NARMAX) respectively to model static and dynamic hysteresis inherent in piezoelectric actuators.
Autoregressive model with exogenous inputs (ARX) is a widely-used black-box type model underlying adaptive predictors and controllers.
The paper provides an approximate estimation of important autoregressive model with exogenous variables (ARX) and uniform noise.
In this methodology, ARX models (Auto-Regressive models with eXogenous input) are created for different sensor clusters by using the free response of the structure.
If C q) = 1,D q) = 1 and F q) = 1, then (1) becomes to be the AutoRegressive model with eXogenous inputs (ARX) model.
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