# Linear Parameter Estimation with Singular Value Decomposition

 ao/linlsqsvd Linear least squares with singular value deconposition - single experiment. matrix/linlsqsvd Linear least squares with singular value deconposition - multiple experiments. matrix/linfitsvd Iterative linear parameter estimation for multichannel systems - symbolic system model in frequency domain. matrix/linfitsvd Iterative linear parameter estimation for multichannel systems - ssm system model in time domain. References

The following sections gives an introduction to the linear parameters estimation methods based on singular value decomposition.

## Linear least squares with singular value deconposition - single experiment.

We report an example of the application of ao/linlsqsvd. The example shows how to perform a linear parameters estimation for a single data series which is representing the output of an experiment on a given physical system.

## Linear least squares with singular value deconposition - multiple experiments.

We report an example of the application of matrix/linlsqsvd. The example shows how to perform a linear parameters estimation for multiple data series which are representing the output of multiple experiments on a given physical system.

## Iterative linear parameter estimation for multichannel systems - symbolic system model in frequency domain.

We report an example of the application of matrix/linfitsvd. The example shows how to perform an iterative linear parameters estimation for a multichannel system. System model is analystic and frequency domain. Fit is performed in time domain. Further details can be found in ref. [1].

## Iterative linear parameter estimation for multichannel systems - ssm system model in time domain.

We report an example of the application of matrix/linfitsvd. The example shows how to perform an iterative linear parameters estimation for a multichannel system. System model is ssm and time domain. Fit is performed in time domain.

## References

1. M Nofrarias, L Ferraioli, G Congedo, Comparison of parameter estimates results in STOC Exercise 6, S2-AEI-TN-3070.