MATLAB: Sum of square error

general matlab usage

Hello,

I'm very new to Matlab. We are attempting to curve fit a biologic response to a sinusoidal input. I'm able to fit the curve using the system identification tool without problems. For this study we need a measured of error. I have been able to get confidence intervals but what I would like to get is the sum of squares error. I have a few questions:

Is there a way to display the sum of square error in Matlab?
What is the number displayed on the model output as a measure of best fit?
When the data is presented following the estimation, it displays the loss function and the FPE. What is the FPE?

Thank you. Michael

Best Answer

What type of model is model1 (what does class(model1) return)? PE is a System Identification Toolbox function that would work only on models recognized by it. You can create either IDPROC or IDPOLY structure for your model.

model = idproc('p1d'); model.Kp = K; model.Td = Td; model.Tp1 = Tp1;

model = idpoly(1,K/Tp1,1,1,[1 1/Tp1],'Ts',0,'noise',0);

Related Solutions

MATLAB: What is the difference between RESID, COMPARE and PE functions in System Identification Toolbox 7.2.1 (R2008b)

RESID and PE functions compute 1-step ahead prediction errors (M=1). For errors

corresponding to arbitrary horizons, you may compute prediction results

yp = predict(model, data, M),

where M is prediction horizon. You could also use COMPARE function too whose first output argument returns the predicted output:

Y = compare(data,model,M)
yp = Y{1};

Note that calling "COMPARE(data, model)" means prediction horizon M is Inf. You can also compute prediction error as difference between measured and predicted outputs as follows:

e = data;
e.y = data.y - yp.y;

MATLAB: How to get the coefficients from a nlarx model constructed using System Identification Toolbox

The coefficients of the nonlinear arx model are stored in the property called "Nonlinearity". In particular, look at m2.Nonlinearity.Parameters. The coefficients stored correspond to a certain equation whose nature depends upon the type of the nonlinearity chosen (tree partition in your case). Type "idprops treepartition", "idprops wavenet" etc for more information on the underlying equations. A couple of comments:

1. The model obtained by 1-step ahead prediction error minimization can be quite different from the one obtained by simulation error minimization. See the "focus" property which lets you toggle between prediction and simulation.

2. NLARX is a nonlinear model. You cannot just convert it into a (linear) transfer function or a state space model. If you want a linear approximation, use the idnlarx/linearize and idnlarx/linapp commands. If you are looking for a way of importing the nonlinear model into Simulink, use the dedicated "Nonlinear ARX" block which is part of System Identification Toolbox blockset.

Some useful links: For insights into the structure of nonlinear models, see:

http://www.mathworks.com/matlabcentral/fileexchange/28618-data-based-modeling-of-nonlinear-dynamic-systems-using-system-identification-toolbox

For setting up the mask parameters for Nonlinear ARX model block in Simulink, see:

http://www.mathworks.com/matlabcentral/answers/42725-nlarx-model-how-to-simulate-in-simulink

http://www.mathworks.com/matlabcentral/answers/51708-problem-with-system-identification-toolbox-and-sim-command

The reference page for idnlarx has lots of information too:

http://www.mathworks.com/help/ident/ref/idnlarx.html

Best Answer

Related Solutions

MATLAB: What is the difference between RESID, COMPARE and PE functions in System Identification Toolbox 7.2.1 (R2008b)

MATLAB: How to get the coefficients from a nlarx model constructed using System Identification Toolbox

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