Algorithm 1: Combine KFs and RBFNNs.
Based on the training data set ($Tr$):	{Inputs, Targets} → {Model’s Forecast, Observations}
for each element in $Tr$ do
Apply the non-linear Kalman filter and obtain $x^{*}$
endfor
Create the Input data for the RBF network	${CorrectedSWH}_t={SWH}_t+B_t{x}^{*}$
Create the training and validation datasets for the RBF	Distinct training and validation datasets for each training. Same for every topology
for each Cluster do
for each penalty parameter do
for each Activation function do
Form the RBF structure.% number of clusters, regularization parameter λ, activation function
while train ≤ maxtrain % Conduct multiple trainings for each structure
Determine the centroids from the training dataset using the K-means++ and compute the widths.        Train network using LLS based on the training data set.
performance → Network’s performance % SSE based on the validation data set
if performance < Initial Value
Set Initial Value equal to performance
The best results for every combination are stored in a cell array. Number of clusters, performance, penalty parameter, activation function, train time, best centers, widths, and external weights.
endif
train → train+1
endwhile
train → 1
endfor
endfor
readjust Initial Value
Endfor
Define the optimal RBF network structure
if several indices in the Total SSE vector display similar results. % Their absolute difference remains smaller than a specified threshold   position → the index with minimum train time   else   position → the minimum SSE index   end
Best RBFNN structure → best results{position}.