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Public Member Functions | Protected Attributes
LinearAnalyticSystemModelGaussianUncertainty Class Reference

Class for linear analytic systemmodels with additive gaussian noise. More...

#include <linearanalyticsystemmodel_gaussianuncertainty.h>

Inheritance diagram for LinearAnalyticSystemModelGaussianUncertainty:
AnalyticSystemModelGaussianUncertainty SystemModel< MatrixWrapper::ColumnVector >

List of all members.

Public Member Functions

 LinearAnalyticSystemModelGaussianUncertainty (LinearAnalyticConditionalGaussian *pdf)
 Constructor.
virtual ~LinearAnalyticSystemModelGaussianUncertainty ()
 Destructor.
void ASet (const MatrixWrapper::Matrix &a)
 Set Matrix A.
void BSet (const MatrixWrapper::Matrix &b)
 Set Matrix B.
const MatrixWrapper::MatrixAGet () const
 Get Matrix A.
const MatrixWrapper::MatrixBGet () const
 Get Matrix B.
MatrixWrapper::Matrix df_dxGet (const MatrixWrapper::ColumnVector &u, const MatrixWrapper::ColumnVector &x)
 Returns F-matrix.
MatrixWrapper::ColumnVector PredictionGet (const MatrixWrapper::ColumnVector &u, const MatrixWrapper::ColumnVector &x)
 Returns prediction of state.
MatrixWrapper::SymmetricMatrix CovarianceGet (const MatrixWrapper::ColumnVector &u, const MatrixWrapper::ColumnVector &x)
 Covariance of system noise.
int StateSizeGet () const
 Get State Size.
bool SystemWithoutInputs () const
 Has the system inputs or not.
ConditionalPdf
< MatrixWrapper::ColumnVector,
MatrixWrapper::ColumnVector > * 
SystemPdfGet ()
 Get the SystemPDF.
void SystemPdfSet (ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector > *pdf)
 Set the SystemPDF.
MatrixWrapper::ColumnVector Simulate (const MatrixWrapper::ColumnVector &x, const MatrixWrapper::ColumnVector &u, int sampling_method=DEFAULT, void *sampling_args=NULL)
 Simulate the system.
MatrixWrapper::ColumnVector Simulate (const MatrixWrapper::ColumnVector &x, int sampling_method=DEFAULT, void *sampling_args=NULL)
 Simulate the system (no input system)
Probability ProbabilityGet (const MatrixWrapper::ColumnVector &x_k, const MatrixWrapper::ColumnVector &x_kminusone, const MatrixWrapper::ColumnVector &u)
 Get the probability of arriving in a next state.
Probability ProbabilityGet (const MatrixWrapper::ColumnVector &x_k, const MatrixWrapper::ColumnVector &x_kminusone)
 Get the probability of arriving in a next state.

Protected Attributes

ConditionalPdf
< MatrixWrapper::ColumnVector,
MatrixWrapper::ColumnVector > * 
_SystemPdf
 ConditionalPdf representing $ P(X_k | X_{k-1}, U_{k}) $.
bool _systemWithoutInputs
 System with no inputs?

Detailed Description

Class for linear analytic systemmodels with additive gaussian noise.

This class represents all systemmodels of the form

\[ x_k = A \times x_{k-1} + B \times u_{k} + N(\mu,\Sigma) \]

Definition at line 33 of file linearanalyticsystemmodel_gaussianuncertainty.h.


Constructor & Destructor Documentation

Constructor.

Precondition:
LinearAnalyticConditionalGaussian should have 1/2 conditional Arguments (checked) and the first conditional argument should be x!
Parameters:
pdfConditional pdf with Gaussian uncertainty

Member Function Documentation

void ASet ( const MatrixWrapper::Matrix a)

Set Matrix A.

This can be particularly useful for time-varying systems

Parameters:
aMatrix a
void BSet ( const MatrixWrapper::Matrix b)

Set Matrix B.

This can be particularly useful for time-varying systems

Parameters:
bMatrix b

Returns F-matrix.

\[ F = \frac{df}{dx} \mid_{u,x} \]

used by kalman filter variants

Parameters:
uThe value of the input in which the derivate is evaluated
xThe value in the state in which the derivate is evaluated
Bug:
Should actually be defined for _any_ continuous system model! There should be a class between this one and system model tout court, not assuming gaussian uncertainty!
Probability ProbabilityGet ( const MatrixWrapper::ColumnVector x_k,
const MatrixWrapper::ColumnVector x_kminusone,
const MatrixWrapper::ColumnVector u 
) [inherited]

Get the probability of arriving in a next state.

Parameters:
x_kthe next state (at time k)
x_kminusonethe current state (at time k-1)
uthe input
Returns:
the probability value
Probability ProbabilityGet ( const MatrixWrapper::ColumnVector x_k,
const MatrixWrapper::ColumnVector x_kminusone 
) [inherited]

Get the probability of arriving in a next state.

(no-input-system)

Parameters:
x_kthe next state (at time k)
x_kminusonethe current state (at time k-1)
Returns:
the probability value
MatrixWrapper::ColumnVector Simulate ( const MatrixWrapper::ColumnVector x,
const MatrixWrapper::ColumnVector u,
int  sampling_method = DEFAULT,
void *  sampling_args = NULL 
) [inherited]

Simulate the system.

Parameters:
xcurrent state of the system
uinput to the system
Returns:
State where we arrive by simulating the system model for 1 step
Parameters:
sampling_methodthe sampling method to be used while sampling from the Conditional Pdf describing the system (if not specified = DEFAULT)
sampling_argsSometimes a sampling method can have some extra parameters (eg mcmc sampling)
Note:
Maybe the return value would better be a Sample<T> instead of a T
MatrixWrapper::ColumnVector Simulate ( const MatrixWrapper::ColumnVector x,
int  sampling_method = DEFAULT,
void *  sampling_args = NULL 
) [inherited]

Simulate the system (no input system)

Parameters:
xcurrent state of the system
Returns:
State where we arrive by simulating the system model for 1 step
Note:
Maybe the return value would better be a Sample<T> instead of a T
Parameters:
sampling_methodthe sampling method to be used while sampling from the Conditional Pdf describing the system (if not specified = DEFAULT)
sampling_argsSometimes a sampling method can have some extra parameters (eg mcmc sampling)
int StateSizeGet ( ) const [inherited]

Get State Size.

Copy constructor SystemModel(const SystemModel<T>& model);

Returns:
the statesize of the system

Get the SystemPDF.

Returns:
a reference to the ConditionalPdf describing the system

Set the SystemPDF.

Parameters:
pdfa reference to the ConditionalPdf describing the system

The documentation for this class was generated from the following file: