Journal of Control Science and Engineering / 2017 / Article / Alg 1

Research Article

Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

Algorithm 1

The detailed algorithm steps of the parameter selection method for support vector regression based on adaptive fusion of mixed kernel function.
The parameter selection method for support
vector regression based on adaptive fusion of
mixed kernel function
Initialization:
() For original data set , select the mixed
kernel function, and set the initial parameter
state value .
() Divide into groups by using fold
cross validation method denoted by .
While (Parameter state value does not meet the
set conditions) do
Time update process:
() Calculate weights , , , using
formulas (6)-(7).
Measurement update process:
() Decompose one step prediction error
covariance matrix and evaluate the
cubature point according to formula () in
reference [19].
() Train the data set based on the LIBSVM
algorithm to obtain the final prediction output.
() Combining predict , compute one step
prediction by using formula (12).
() Use formula ()–() of reference [19] to
implement the subsequent measurement update.
End while
End

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