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State-Space Models
Apps
| System Identification | Identify models of dynamic systems from measured data |
Functions
ssest | Estimate state-space model using time or frequency domain data |
ssregest | Estimate state-space model by reduction of regularized ARX model |
n4sid | Estimate state-space model using subspace method |
idss | State-space model with identifiable parameters |
pem | Prediction error estimate for linear and nonlinear model |
delayest | Estimate time delay (dead time) from data |
getpvec | Model parameters and associated uncertainty data |
setpvec | Modify value of model parameters |
getpar | Obtain attributes such as values and bounds of linear model parameters |
setpar | Set attributes such as values and bounds of linear model parameters |
ssform | Quick configuration of state-space model structure |
init | Set or randomize initial parameter values |
idpar | Create parameter for initial states and input level estimation |
idssdata | State-space data of identified system |
findstates | Estimate initial states of model |
ssestOptions | Option set for ssest |
ssregestOptions | Option set for ssregest |
n4sidOptions | Option set for n4sid |
findstatesOptions | Option set for findstates |
Examples and How To
Estimate State-Space Model With Order Selection
To estimate a state-space model, you must provide a value of its order, which represents the number of states.
Estimate State-Space Models in System Identification App
Import data into the System Identification app.
Estimate State-Space Models at the Command Line
Perform black-box or structured estimation.
Estimate State-Space Models with Free-Parameterization
The default parameterization of the state-space matrices A, B, C, D, and K is free; that is, any elements in the matrices are adjustable by the estimation routines.
Estimate State-Space Models with Canonical Parameterization
Canonical parameterization represents a state-space system in a reduced parameter form where many elements of A, B and C matrices are fixed to zeros and ones.
Estimate State-Space Models with Structured Parameterization
Structured parameterization lets you exclude specific parameters from estimation by setting these parameters to specific values.
Estimate State-Space Equivalent of ARMAX and OE Models
This example shows how to estimate ARMAX and OE-form models using the state-space estimation approach.
Concepts
State-space models are models that use state variables to describe a system by a set of first-order differential or difference equations, rather than by one or more nth-order differential or difference equations.
Data Supported by State-Space Models
You can use time-domain and frequency-domain data that is real or complex and has single or multiple outputs.
Supported State-Space Parameterizations
System Identification Toolbox™ software supports the following parameterizations that indicate which parameters are estimated and which remain fixed at specific values:
Specifying Initial States for Iterative Estimation Algorithms
When you estimate state-space models, you can specify how the algorithm treats initial states.
State-Space Model Estimation Methods
Choose between noniterative subspace methods, iterative method that uses prediction error minimization algorithm, and noniterative method.
Identifying State-Space Models with Separate Process and Measurement Noise Descriptions
An identified linear model is used to simulate and predict system outputs for given input and noise signals.
