Mackey-Glass-Time-Series-Forecasting.rar_matlab例程_matlab_

% Nomenclature % --------------- % st1 => singleton type-1 FLS % fcs => forecasted series % mydata => MG time series sample data % TO RUN % --------- % mydata = mgts(3000); % n = 0; % RMSE = ttsf(mydata) function fcs = st1(mydata, n) for i = 1:504 % Training Data points 504 tgd(i) = mydata(i+n); end k = 0; for i = 1:500 for j = 1:5 s(i, j) = tgd(k+j); end k = k+1; end M = 500; % No. of Rule pairs x = 0:.01:2; % hold on; for j = 1:M fs1 = gaussmf(x, [0.1 s(j, 1)]); fs2 = gaussmf(x, [0.1 s(j, 2)]); fs3 = gaussmf(x, [0.1 s(j, 3)]); fs4 = gaussmf(x, [0.1 s(j, 4)]); fs5 = gaussmf(x, [0.1 s(j, 5)]); R(j, :) = fs1.*fs2.*fs3.*fs4.*fs5; Memg(j) = max(R(j, :)); % plot(x, R(j,:)) end % hold off; for j=1:M for i = 1:201 if max(R(j, i)) == max(R(j, :)) y(j) = i*0.01; end end end % Height Defuzzification Method Snum = 0; Sden = 0; for j = M Snum = Snum+y(j)*Memg(j); Sden = Sden + Memg(j); end HDfuzz = Snum/Sden; fcs = HDfuzz; % ----------------------------------------- % For Better Display % ----------------------------------------- dsp = n; disp('~~~~~~~') disp(' ') disp(' FCast ') disp (dsp) % ------------------------------------------------- % End of st1(mydata, n) % -------------------------------------------------