Filter and Data container for Recursive Least Sqaure Alssm Filters in Steady State Mode

With RLSAlssmSteadyState a common $$W_k = W_{steady}$$ is used for all samples (faster computation). Note that using a common $$W_k$$ potentially leads to border missmatch effects and to completely invalid results when samples have individual sample weights.

Methods

 __init__(cost_model[, steady_state_method]) eval_errors(xs[, ks]) Evaluation of the squared error for multiple state vectors xs. filter(y[, v]) Computes the intermediate parameters for subsequent squared error computations and minimizations. filter_minimize_v(y[, v, H, h]) filter_minimize_x(y[, v, H, h]) minimize_v([H, h, return_constrains]) Returns the state vector v of the squared error minimization with linear constraints minimize_x([H, h]) Returns the state vector x of the squared error minimization with linear constraints set_backend(backend) Setting the backend computations option

Attributes

 W Filter Parameter $$W$$ betas Segment scalars weights the cost function per segment cost_model Cost Model kappa Filter Parameter $$\kappa$$ nu Filter Parameter $$\nu$$ xi Filter Parameter $$\xi$$
property W#

Filter Parameter $$W$$

Type

ndarray

property betas#

Segment scalars weights the cost function per segment

Type

ndarray

property cost_model#

Cost Model

Type
eval_errors(xs, ks=None)#

Evaluation of the squared error for multiple state vectors xs.

RLSAlssm.eval_error

filter(y, v=None)#

Computes the intermediate parameters for subsequent squared error computations and minimizations.

Computes the intermediate parameters using efficient forward- and backward recursions. The results are stored internally, ready to solve the least squares problem using e.g., minimize_x() or minimize_v(). The parameter allocation allocate() is called internally, so a manual pre-allcation is not necessary.

Parameters

K : number of samples
L : output order / number of signal channels
S : number of signal sets

filter_minimize_v(y, v=None, H=None, h=None)#

This method has the same output as calling the methods

rls.filter(y)
xs = rls.minimize_v()

filter_minimize_x(y, v=None, H=None, h=None)#
property kappa#

Filter Parameter $$\kappa$$

Type

ndarray

minimize_v(H=None, h=None, return_constrains=False)#

Returns the state vector v of the squared error minimization with linear constraints

minimize_x(H=None, h=None)#

Returns the state vector x of the squared error minimization with linear constraints

property nu#

Filter Parameter $$\nu$$

Type

ndarray

set_backend(backend)#

Setting the backend computations option

Parameters

backend (str) – ‘py’, for python backend, ‘jit’ for Just in Time backend

property xi#

Filter Parameter $$\xi$$

Type

ndarray