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