ProcessModel (Apache Commons Math 4.0-beta1)

All Known Implementing Classes:

DefaultProcessModel
```
public interface ProcessModel
```
Defines the process dynamics model for the use with a KalmanFilter.

Since:

3.0

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method	Description
`RealMatrix`	`getControlMatrix()`	Returns the control matrix.
`RealMatrix`	`getInitialErrorCovariance()`	Returns the initial error covariance matrix.
`RealVector`	`getInitialStateEstimate()`	Returns the initial state estimation vector.
`RealMatrix`	`getProcessNoise()`	Returns the process noise matrix.
`RealMatrix`	`getStateTransitionMatrix()`	Returns the state transition matrix.

- Method Detail
  - getStateTransitionMatrix
```
RealMatrix getStateTransitionMatrix()
```
    Returns the state transition matrix.
    
    Returns:
    
    the state transition matrix
  - getControlMatrix
```
RealMatrix getControlMatrix()
```
    Returns the control matrix.
    
    Returns:
    
    the control matrix
  - getProcessNoise
```
RealMatrix getProcessNoise()
```
    Returns the process noise matrix. This method is called by the KalmanFilter every prediction step, so implementations of this interface may return a modified process noise depending on the current iteration step.
    
    Returns:
    
    the process noise matrix
    
    See Also:
    
    KalmanFilter.predict(), KalmanFilter.predict(double[]), KalmanFilter.predict(RealVector)
  - getInitialStateEstimate
```
RealVector getInitialStateEstimate()
```
    Returns the initial state estimation vector.
    Note: if the return value is zero, the Kalman filter will initialize the state estimation with a zero vector.
    
    Returns:
    
    the initial state estimation vector
  - getInitialErrorCovariance
```
RealMatrix getInitialErrorCovariance()
```
    Returns the initial error covariance matrix.
    Note: if the return value is zero, the Kalman filter will initialize the error covariance with the process noise matrix.
    
    Returns:
    
    the initial error covariance matrix