Dynamic |
The DynamicParameterEstimation type exposes the following members.
Methods| Name | Description | |
|---|---|---|
 | CalculateCrossPredictionError(IReadOnlyListDouble, IReadOnlyListDouble) | With the already evaluated parameters, calculates the mean error for another piece of data. Note that both vectors must have a length of at least _startingPoint + 1, since the first _startingPoint samples are used for the history. |
| CalculateCrossPredictionError(IReadOnlyListDouble, IReadOnlyListDouble, IVectorDouble) | With the already evaluated parameters, calculates the mean error for another piece of data. Note that both vectors must have a length of at least _startingPoint + 1, since the first _startingPoint samples are used for the history. |
 | CalculateNumberOfData | Calculates the number of points that can be used on the right side of the linear equation (i.e. the number of rows of the equation). With the same length of x and y, the number of usable data points decreases when more x or y parameters are evaluated, since more samples are needed for the history and those samples cannot be used on the right side of the equation. |
| CalculatePredictionError | Calculates the mean prediction error, i.e. Sqrt(Sum((y-yPredicted)²)/N). |
| CalculatePredictionError(VectorDouble) | Calculates the mean prediction error, i.e. Sqrt(Sum((y-yPredicted)²)/N). |
| CalculateResultingParameter | Calculates the resulting parameter array by calling the solver. |
| CalculateSelfPredictionError | Calculates the mean prediction error using recursive prediction of y (self-prediction). |
| CalculateSelfPredictionError(IVectorDouble) | Calculates the mean prediction error using recursive prediction of y (self-prediction). |
| CalculateSelfPredictionError(MatrixDouble, IReadOnlyListDouble, IVectorDouble) | Calculates the mean prediction error using recursive prediction of y (self-prediction) for the given matrix and comparison y values. |
| CalculateStartingPoint | Calculates the starting point, i.e. the first index in the y array that can be used for the right side of the linear equation. The starting point increases when more x or y parameters are evaluated, since more "history" samples are needed in this case. |
| Equals | Determines whether the specified object is equal to the current object. (Inherited from Object) |
| EstimateParameterByBurgsAlgorithm | Estimates the parameters using Burg's algorithm. |
 | Extrapolate | Extrapolates y-values until the end of the vector by using linear prediction. |
| FillBacksubstitutionY | Fills the back-substitution array with data from the provided y vector. |
| FillInputMatrix | Fills the input matrix, i.e. the left side of the linear equation. |
| Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object) |
| GetFrequencyResponse | Calculates the frequency response for a given frequency. |
| GetHashCode | Serves as the default hash function. (Inherited from Object) |
| GetTransferFunction | Gets the impulse response to a pulse at t=0, i.e. to x[0]==1, x[1]..x[n]==0. The background component is not taken into account. |
| GetType | Gets the Type of the current instance. (Inherited from Object) |
| MakeEstimation | Calculates the dynamic parameter estimation. |
| MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object) |
| SetHelperMembers | Sets all helper values such as _numX, _numY, _backgroundOrderPlus1, _numberOfParameter, and _startingPoint. |
| ToString | Returns a string that represents the current object. (Inherited from Object) |
See Also