Created on Fri Jul 28 2017
@author: Giuseppe Armenise
Classes:
| Name | Description |
|---|---|
Armax |
|
Armax(
G: TransferFunction,
H: TransferFunction,
*order_bounds: tuple[int, int],
Vn: float | floating,
y_id: ndarray,
method: ICMethods = "AIC"
)
The AutoRegressive-Moving-Average with eXogenous inputs model is computed based on a recursive lest-square regression between the input data (U) and the measured output data (Y). As Y is noisy in practice, a white noise (E) is identified within the model. This model is designed to deal with potential time-delays between U and Y.
SIPPY implements an iterative procedure, with extit{iterative least-square regression} = ILLS.
The following equations summarize the equations involved in the model:
$$ Y = G.U + H.E
G = B / A H = C / A
A = 1 + a_1z^(-1) + ... + a_naz^(-na) B = b_1z^(-1-theta) + ... + b_nbz^(-nb-theta) C = c_1z^(-1) + ... + c_ncz^(-nc) $$
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
|
TransferFunction
|
output response |
required |
|
TransferFunction
|
noise response |
required |
|
tuple[int, int]
|
extended range of the order of: - na_bounds: the common denominator - nb_bounds: the G numerator - nc_bounds: the H numerator - theta_bounds: the discrete theta in B |
()
|
|
float | floating
|
The estimated error norm. |
required |
|
ndarray
|
The model output including non-identified outputs. |
required |
|
ICMethods
|
Method used of to attribute a performance to the model |
'AIC'
|