If the data from the Support Vector Regression (SVR) model is available, it is possible to train an Adaptive Neuro-Fuzzy Inference System (ANFIS) model.
%% SVR data
x = linspace(-1, 1, 2001)';
y = asinh(50*x);
mdl = fitrsvm(x, y, 'Standardize', true, 'KernelFunction', 'gaussian', 'KernelScale', 'auto');
yfit= predict(mdl, x);
figure
plot(x, yfit), grid on
xlabel('Input'), ylabel('Output'), title('Data from SVR')
%% create initial FIS
genOpt = genfisOptions('GridPartition');
genOpt.NumMembershipFunctions = 4;
genOpt.InputMembershipFunctionType = 'gauss2mf';
genOpt.OutputMembershipFunctionType = 'constant';
inFIS = genfis(x, yfit, genOpt);
%% train ANFIS
opt = anfisOptions('InitialFIS', inFIS, 'EpochNumber', 50);
opt.DisplayANFISInformation = 0;
opt.DisplayErrorValues = 0;
opt.DisplayStepSize = 0;
opt.DisplayFinalResults = 0;
outFIS = anfis([x yfit],opt);
figure
plotrule(outFIS)
%% plot result
figure
plot(x, [yfit, evalfis(outFIS, x)]), grid on
xlabel('Input'), ylabel('Output'),
title('Compare SVR Data with Trained ANFIS Output')
legend('SVR Data', 'Trained ANFIS Output', 'location', 'southeast')
