Answered

LOES of a high order state space model

You could attempt this in frequency domain. G = frd(OriginalModel,w) % w is a suitably chosen frequency grid ReducedModel = ss...

LOES of a high order state space model

You could attempt this in frequency domain. G = frd(OriginalModel,w) % w is a suitably chosen frequency grid ReducedModel = ss...

9 mois ago | 1

| accepted

Answered

How to build a single input multiple output ARX or ARMAX model with MATLAB

Identification routines want variables to be arranged along the columns, time along rows. Your data "y" is transposed. Also, the...

How to build a single input multiple output ARX or ARMAX model with MATLAB

Identification routines want variables to be arranged along the columns, time along rows. Your data "y" is transposed. Also, the...

10 mois ago | 0

| accepted

Answered

Use of modalfrf for operating measurements

This is essentially a system identification problem. You have non-parametric (tfestimate, spa, spafdr, etfe) techniques and para...

Use of modalfrf for operating measurements

This is essentially a system identification problem. You have non-parametric (tfestimate, spa, spafdr, etfe) techniques and para...

10 mois ago | 0

| accepted

Answered

How to remove delay from idtf object?

You will need an invertible realization. Try high order transfer function estimations with no delays. Or, if the estimated model...

How to remove delay from idtf object?

You will need an invertible realization. Try high order transfer function estimations with no delays. Or, if the estimated model...

10 mois ago | 0

| accepted

Answered

Testing different optimization methods on a state space model by solving ODEs

The closest infrastructure to this style of numerical optimization approach is "grey box identification". See idgrey, idnlgrey, ...

Testing different optimization methods on a state space model by solving ODEs

The closest infrastructure to this style of numerical optimization approach is "grey box identification". See idgrey, idnlgrey, ...

10 mois ago | 0

| accepted

Answered

nlarx model initial conditions

You can prefix estimation data (both input and output signals) with nd zeros, where nd = maximum lag in the model. Initial condi...

nlarx model initial conditions

You can prefix estimation data (both input and output signals) with nd zeros, where nd = maximum lag in the model. Initial condi...

10 mois ago | 0

| accepted

Answered

How to force tfest to estimate the process with "only real poles" ?

TFEST cannot guarantee real poles. If you can work with <=3 poles and <=1 zero, try PROCEST. This is a process model estimator...

How to force tfest to estimate the process with "only real poles" ?

TFEST cannot guarantee real poles. If you can work with <=3 poles and <=1 zero, try PROCEST. This is a process model estimator...

11 mois ago | 3

| accepted

Answered

What is the difference between FRD and IDFRD

Both FRD and IDFRD are used to store Freqyency Response Data, that is, the complex frequency response vector (Mag.*exp(i*Phase))...

What is the difference between FRD and IDFRD

Both FRD and IDFRD are used to store Freqyency Response Data, that is, the complex frequency response vector (Mag.*exp(i*Phase))...

11 mois ago | 1

| accepted

Answered

How to estimate the parameter in a customized transfer function

Grey-box identification is an option. You will need to write a function that takes K0 and a0 as inputs, and returns state-space ...

How to estimate the parameter in a customized transfer function

Grey-box identification is an option. You will need to write a function that takes K0 and a0 as inputs, and returns state-space ...

11 mois ago | 0

Answered

Minimum input data resolution

Look up Nyquist Sampling Theorem. If you are sampling (hopefully with anti-aliasing) at 1Hz then you cannot theoretically captur...

Minimum input data resolution

Look up Nyquist Sampling Theorem. If you are sampling (hopefully with anti-aliasing) at 1Hz then you cannot theoretically captur...

11 mois ago | 0

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Answered

Well-identified fitted process model does not behave like data on simulink

You are almost there. Convert the model into state-space form and use it for simulation. For initial conditions, you will need t...

Well-identified fitted process model does not behave like data on simulink

You are almost there. Convert the model into state-space form and use it for simulation. For initial conditions, you will need t...

11 mois ago | 0

| accepted

Answered

How to calculate the transfer function for a 16 input system?

Try also TFEST. Although you might want to reduce the number of inputs by PCA or PLS analysis.

How to calculate the transfer function for a 16 input system?

Try also TFEST. Although you might want to reduce the number of inputs by PCA or PLS analysis.

11 mois ago | 0

Answered

nlarx model compare and predict (horizon kept 1) fit totally differs

The difference between (finite-horizon) prediction and simulation is a fundamental concept, something you could read books/artic...

nlarx model compare and predict (horizon kept 1) fit totally differs

The difference between (finite-horizon) prediction and simulation is a fundamental concept, something you could read books/artic...

11 mois ago | 0

| accepted

Answered

Can nlgreyest() estimate open-loop unstable models?

With greyest, either parameterize K matrix using the ODE function, or choose to esitmate it separately by using the "Disturbance...

Can nlgreyest() estimate open-loop unstable models?

With greyest, either parameterize K matrix using the ODE function, or choose to esitmate it separately by using the "Disturbance...

12 mois ago | 0

Answered

System Identification of Closed Loop Data and Unstable Plant

The first reference: [1] System Identification — Theory For the User, Lennart Ljung, Section 13.4-13.5, 2nd ed, PTR Prentice Ha...

System Identification of Closed Loop Data and Unstable Plant

The first reference: [1] System Identification — Theory For the User, Lennart Ljung, Section 13.4-13.5, 2nd ed, PTR Prentice Ha...

12 mois ago | 0

| accepted

Answered

Accessing the GUI function programatically.

I will repeat Aditya Baru's comment as an answer. The App now supports MATLAB code generation (creating a function from the tas...

Accessing the GUI function programatically.

I will repeat Aditya Baru's comment as an answer. The App now supports MATLAB code generation (creating a function from the tas...

12 mois ago | 1

| accepted

Answered

How to set parameters of Recursive Polynomial Model Estimator in Simulink

The Recursive Polynomial Model Estimator supports single output estimations only.

How to set parameters of Recursive Polynomial Model Estimator in Simulink

The Recursive Polynomial Model Estimator supports single output estimations only.

12 mois ago | 0

| accepted

Answered

System Identification - Frequency Domain

Make an attempt with stability enforced. opt = tfestOptions('EnforceStability', true); model=tfest(f_data,6,opt) Also, you ...

System Identification - Frequency Domain

Make an attempt with stability enforced. opt = tfestOptions('EnforceStability', true); model=tfest(f_data,6,opt) Also, you ...

12 mois ago | 2

| accepted

Answered

System Identification Toolboox error dialog

What dataset are you using for validation? Does it suply the inputs and outputs that the model needs?

System Identification Toolboox error dialog

What dataset are you using for validation? Does it suply the inputs and outputs that the model needs?

12 mois ago | 0

Answered

Does the order (index) of inputs and outputs matter in MIMO system identification?

Yes the order matters since within a given noise level, there are many models that can explain the data. Settings related to sea...

Does the order (index) of inputs and outputs matter in MIMO system identification?

Yes the order matters since within a given noise level, there are many models that can explain the data. Settings related to sea...

12 mois ago | 0

| accepted

Answered

System Identification Toolbox: How can we modify the starting parameters for the armax-algorithm?

You can set the A, B, C values explicitly, as in estimatedPolymodel.A = ARCoeff Or, call the IDPOLY constructor with A, B, C p...

System Identification Toolbox: How can we modify the starting parameters for the armax-algorithm?

You can set the A, B, C values explicitly, as in estimatedPolymodel.A = ARCoeff Or, call the IDPOLY constructor with A, B, C p...

environ un an ago | 0

| accepted

Answered

How to identify a sytem by the System Identification Toolbox that is invertable ?

Tyically yes. If you are estimating state-space model, use "feedthough" name-value pair, as in ssest(Data, order, 'Feedthrough'...

How to identify a sytem by the System Identification Toolbox that is invertable ?

Tyically yes. If you are estimating state-space model, use "feedthough" name-value pair, as in ssest(Data, order, 'Feedthrough'...

environ un an ago | 0

Answered

how to convert a xls to a data ensemble for import into diagnostic feature app

If the data is not too big to fit into MATLAB memory, I would suggest importing it into MATLAB first as a set of tables or timet...

how to convert a xls to a data ensemble for import into diagnostic feature app

If the data is not too big to fit into MATLAB memory, I would suggest importing it into MATLAB first as a set of tables or timet...

environ un an ago | 0

Answered

How can I find initial states for simulation?

Initial states show the effect of the environment on the system. They are not a property of the system to be determined uniquely...

How can I find initial states for simulation?

Initial states show the effect of the environment on the system. They are not a property of the system to be determined uniquely...

environ un an ago | 1

| accepted

Answered

System Identification toolbox: how to print estimated ARMA-coefficients for each iteration step in armax-algorithm

Use "full" display option, as in: opt=armaxOptions('Display','full'); estimatedPolymodel=armax(iddata(outputdata,inputdata,tsa...

System Identification toolbox: how to print estimated ARMA-coefficients for each iteration step in armax-algorithm

Use "full" display option, as in: opt=armaxOptions('Display','full'); estimatedPolymodel=armax(iddata(outputdata,inputdata,tsa...

environ un an ago | 1

| accepted

Answered

how to use state space model?

If you want to reproduce the response of "predict" by (manual) simulation, you will need to generate the right prediction model ...

how to use state space model?

If you want to reproduce the response of "predict" by (manual) simulation, you will need to generate the right prediction model ...

environ un an ago | 1

Answered

how to plot on the same bode plot a manual function plot() with function bode()?

You could try: G = frd(f1(w),w); % assuming f1(w) is a complex numeric vector bode(G,f2)

how to plot on the same bode plot a manual function plot() with function bode()?

You could try: G = frd(f1(w),w); % assuming f1(w) is a complex numeric vector bode(G,f2)

environ un an ago | 0

Answered

Residual analysis of 100% fit model using system identification toolbox

With simulated data with no noise, it is difficult to read the residual results since there is no baseline noise floor. That is,...

Residual analysis of 100% fit model using system identification toolbox

With simulated data with no noise, it is difficult to read the residual results since there is no baseline noise floor. That is,...

environ un an ago | 0

| accepted

Answered

Fitting complex function to measurement values

Let X be your data matrix. [~,I]=unique(X(:,1),'stable'); h=X(I,2).*exp(1i*X(I,3)/180*pi); w=X(I,1)*2*pi; G=idfrd(h,w,'Ts',0...

Fitting complex function to measurement values

Let X be your data matrix. [~,I]=unique(X(:,1),'stable'); h=X(I,2).*exp(1i*X(I,3)/180*pi); w=X(I,1)*2*pi; G=idfrd(h,w,'Ts',0...

environ un an ago | 0

Answered

MISO system identification tool box step response

Yes, use LSIM with input: U = [u, u], where u is a step signal.

MISO system identification tool box step response

Yes, use LSIM with input: U = [u, u], where u is a step signal.

environ un an ago | 0

| accepted