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With such an assumption, auto-regressive models with exogenous variables (ARX) may fit well to the data.
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.
This paper proposes to use the nonlinear-autogressive models with exogenous input (NARX) model to predict the hysteretic behaviour of passive control systems.
These requirements are met by using auto regressive models with exogenous inputs and time series auto-regressive models to simulate the inside and outside climate conditions, respectively.
The research finds its theoretical roots in robot training and system identification techniques such as ARMAX (Auto-Regressive Moving Average models with eXogenous inputs) and NARMAX (Non-linear ARMAX).
Additionally, empirical models are developed that relate equipment variables, the conditioned environment and a building's surroundings and occupation characteristics using representation through transfer functions and auto-regressive models with exogenous inputs (ARX).
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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.
A moving-average model with exogenous input (MAX) is given to describe the transmission channel and colored noise.
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