Try this:
D = load('Stashu Kozlowski DHM.mat'); % File Attached
x = D.x;
y = D.y;
y = detrend(y); % Remove Linear Trend
yu = max(y);
yl = min(y);
yr = (yu-yl); % Range of ‘y’
yz = y-yu+(yr/2);
zci = @(v) find(v(:).*circshift(v(:), [-1 0]) <= 0); % Returns Approximate Zero-Crossing Indices Of Argument Vector
zt = x(zci(y));
per = 2*mean(diff(zt)); % Estimate period
ym = mean(y); % Estimate offset
fit = @(b,x) b(1) .* exp(b(2).*x) .* (sin(2*pi*x./b(3) + 2*pi/b(4))) + b(5); % Objective Function to fit
fcn = @(b) norm(fit(b,x) - y); % Least-Squares cost function
[s,nmrs] = fminsearch(fcn, [yr; -10; per; -1; ym]) % Minimise Least-Squares
xp = linspace(min(x),max(x), 500);
figure
plot(x,y,'b', 'LineWidth',1.5)
hold on
plot(xp,fit(s,xp), '--r')
hold off
grid
xlabel('Time')
ylabel('Amplitude')
legend('Original Data', 'Fitted Curve')
text(0.3*max(xlim),0.7*min(ylim), sprintf('$y = %.3f\\cdot e^{%.0f\\cdot x}\\cdot sin(2\\pi\\cdot x\\cdot %.0f%.3f)$', [s(1:2); 1./s(3:4)]), 'Interpreter','latex')
The estimated parameters are:
s =
-1.398211481931498e+00
-6.142349926864338e+02
2.591368008158479e-04
-5.442228857001487e+00
-3.075267405594925e-15
and the fit is nearly perfect:
