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Support vector machine regression mapping function for nonlinear ARX models (requires Statistics and Machine Learning Toolbox)

Since R2022a


An idSupportVectormachine object implements a support vector machine (SVM) regression model, and is a nonlinear mapping function for estimating nonlinear ARX models. This mapping object, which is also referred to as a nonlinearity, incorporates objects that the mapping function creates using Statistics and Machine Learning Toolbox™ functions. The mapping object contains a single component—the nonlinear component that the SVM algorithm represents.

The input to the mapping object can be a vector of the regressors of a nonlinear ARX model.

Diagram of an idSupportVectorMachine object with a kernel function that produces an output.

Mathematically, idSupportVectorMachine is a function that maps m inputs X(t) = [x(t1),x2(t),…,xm(t)]T to a scalar output y(t) using the following relationship:


Here, S(X(t)) is the regressive SVM function that constitutes the kernel of the idSupportVectorMachine object, and can be expressed as:



  • X is an m-by-1 vector of inputs, or regressors.

  • N is the number of support vectors in the trained model.

  • xn is the nth support vector in the trained model.

  • αn is the weight associated with each support vector.

  • G is the Gram matrix that results from the operation of the specified kernel function on X and xn.

  • b is the offset of the trained model.

The SVM mapping object implements linear epsilon-insensitive SVM regression, which is particularly robust to outliers. In this type of regression, a primary goal of the algorithm is to find a function f(x) that deviates from a training response yn by a value no greater than ε for each training point. The algorithm accommodates situations where no such linear function is available by introducing “slack variables” ζ and ζ* that provide a softer margin beyond ε. The corresponding loss function, known as the ε-insensitive loss function, ignores observed values that are within ε by treating them as equal to 0. The loss, Lε, is based only on the distance between the observed value y and the ε boundary when the value is beyond that boundary, as described by:

Lε={0if |yf(x)|ε|yf(x)|εotherwise

For more information about SVM regression models, see Understanding Support Vector Machine Regression (Statistics and Machine Learning Toolbox).

Use idSupportVectorMachine as the value of the OutputFcn argument of an idnlarx model. For example, specify idSupportVectorMachine when you estimate an idnlarx model with the following command.

sys = nlarx(data,regressors,idSupportVectorMachine)

You can configure the idSupportVectorMachine object to fix parameters during estimation and modify options. For example, if you are using a previously estimated idSupportVectorMachine model S and want to retain the model parameters, fix the parameters during idnlarx estimation by setting the S.Free property to false. To modify an estimation option, set the value of the option property in S.EstimationOptions. For example, to change the solver to 'ISDA', use the command S.EstimationOptions.Solver = 'ISDA'. Use evaluate to compute the output of the function for a given vector of inputs.




S = idSupportVectorMachine creates an idSupportVectorMachine object S with the kernel function 'Gaussian' and default kernel parameters. The number of inputs is determined during model estimation and the number of outputs is 1.

S = idSupportVectorMachine(KernelFunction) specifies the specific kernel KernelFunction.

S = idSupportVectorMachine(KernelFunction,EpsilonMargin) specifies the property EpsilonMargin, which is half the width of the epsilon-insensitive band.


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Input signal names for the inputs to the mapping object, specified as a 1-by-m cell array, where m is the number of input signals. This property is determined during estimation.

Output signal name for the output of the mapping object, specified as a 1-by-1 cell array. This property is determined during estimation.

SVM kernel function name, specified as 'Gaussian', 'rbf', 'Linear', or 'Polynomial'. The SVM algorithm uses the kernel function to compute the Gram matrix.

Kernel scale parameter, specified as 'auto' or a positive scalar. The software divides elements of the predictor matrix by this value prior to computing the Gram matrix. KernelScale is an important tuning parameter for SVM models.

When you specify 'auto', the software uses the fitrsvm 'auto' setting.

Half the width of the epsilon-insensitive band, specified as 'auto' or a nonnegative scalar. For more information on this band, see Understanding Support Vector Machines (Statistics and Machine Learning Toolbox).

Polynomial kernel function order, specified as a positive integer that the software uses when KernelFunction is 'Polynomial'.

Kernel offset order, specified as a nonnegative scalar. The software adds KernelOffset to each element of the Gram matrix.

Box constraint that is the upper bound for the absolute value of the alpha coefficients, specified as a positive scalar.

Support vector parameters of the trained SVM model, specified as shown in the following table.


Initial estimates of the α weights associated with the support vectors, specified as a numeric vector with length equal to the number of rows in the estimation data set.

You can set the values of Alpha only prior to estimation.

LinearCoefficientLinear coefficients that the software estimates when KernelFunction is 'Linear'.Read-only property
BiasSVM model offset term that the software estimates.Read-only property
IsSupportVectorLogical vector with length equal to the number of observations that indicates which observations are support vectors.Read-only property

Option to free or fix SVM model parameters during estimation, specified as a logical scalar. If all the parameters have finite values, such as when the idSupportVectorMachine object corresponds to a previously estimated model, then setting Free to false causes the parameters of the kernel G(X) to remain unchanged during estimation. The default value is true.

Estimation options for the idSupportVectorMachine model, specified as shown in the following table. For more information on any of these options, see fitrsvm (Statistics and Machine Learning Toolbox).

OutlierFractionExpected proportion of outliers in the training data, specified as a numeric scalar in the interval [0,1] [ ]
CacheSizeCache size in MB that the software reserves in memory for training the model, specified as a positive scalar.1000

Solver used for parameter estimation, specified as one of the following values:

  • 'SMO' — Sequential Minimal Optimization

  • 'ISDA' — Iterative Single Data Algorithm

  • 'ISDA', if you set OutlierFraction to a positive value

  • 'SMO', otherwise


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Load the data z2.

load iddata2 z2

Create an idSupportVectorMachine mapping object.

S = idSupportVectorMachine
S = 
Support Vector Machine Function

 Support Vector Machine function using a Gaussian kernel

       KernelFunction: 'Gaussian'
        EpsilonMargin: 'auto'
      PolynomialOrder: 3
         KernelOffset: 0
        BoxConstraint: 1
          KernelScale: 'auto'
           Parameters: '<Support vector parameters>'
                 Free: 1
    EstimationOptions: '<Estimation option set>'

Set the KernelScale property to 5.

S.KernelScale = 5;

Estimate a nonlinear ARX model that uses S as the output function.

sys = nlarx(z2,[4 4 1],S);

View the properties of sys.OutputFcn.

ans = 
Support Vector Machine Function
Inputs: y1(t-1), y1(t-2), y1(t-3), y1(t-4), u1(t-1), u1(t-2), u1(t-3), u1(t-4)
Output: y1(t)

 Support Vector Machine function using a Gaussian kernel

       KernelFunction: 'Gaussian'
        EpsilonMargin: 1.0424
      PolynomialOrder: 3
         KernelOffset: 0
        BoxConstraint: 1
          KernelScale: 5
           Parameters: '<Support vector parameters>'
                 Free: 1
    EstimationOptions: '<Estimation option set>'

Compare sys with z2.


Version History

Introduced in R2022a