|System Identification||Identify models of dynamic systems from measured data|
|Transfer function estimation|
|Transfer function model with identifiable parameters|
|Prediction error estimate for linear and nonlinear model|
|Estimate time delay (dead time) from data|
|Model parameters and associated uncertainty data|
|Modify value of model parameters|
|Obtain attributes such as values and bounds of linear model parameters|
|Set attributes such as values and bounds of linear model parameters|
|Set or randomize initial parameter values|
|Option set for tfest|
This topic shows how to estimate transfer function models in the System Identification app.
This topic shows how to estimate transfer function models at the command line.
This example shows how to identify a transfer function containing a specified number of poles for given data.
This example shows how to identify a transfer function to fit a given frequency response data (FRD) containing additional phase roll off induced by input delay.
This example shows how to estimate a transfer function model when the structure of the expected model is known and apply constraints to the numerator and denominator coefficients.
This example shows how to estimate transfer function models with I/O delays.
This example shows how to estimate a transfer function model with unknown transport delays and apply an upper bound on the unknown transport delays.
Improve frequency-domain model estimation by preprocessing data and applying frequency-dependent weighting filters.
Transfer function models describe the relationship between the inputs and outputs of a system using a ratio of polynomials. The model order is equal to the order of the denominator polynomial. The roots of the denominator polynomial are referred to as the model poles. The roots of the numerator polynomial are referred to as the model zeros.
Characteristics of estimation data for transfer function identification.
Specify the values and constraints for the numerator, denominator and transport delays.
Specify how initial conditions are handled during model estimation in the app and at the command line.
An identified linear model is used to simulate and predict system outputs for given input and noise signals. The input signals are measured while the noise signals are only known via their statistical mean and variance. The general form of the state-space model, often associated with Kalman filtering, is an example of such a model, and is defined as: