MATLAB: MATLAB: 2-D plot with z-axis given in color

colormapcolorplotfor loopplot

Best Answer

I am not certain exactly what you want to do, but it would seem to be ideal for the contour function. You can also use surf with the appropriate view setting. (Experiment with that interactively.) I would experiment with image rather than pcolor, because pcolor treats your values as the edges of the segments (so you get one fewer of them in each dimension) rather than the centres.

(Indeed your Question is not spam, so I removed that. The spam filter isn’t perfect.)

Related Solutions

MATLAB: Curve fitting by Genetic Algorithm

I can fit part of your data, however not all of it, because there is some noise in elements 19 through 24 that your function is likely to not able to account for. (I had to eliminate the commas from your data and translate them into decimal points. Thjat worked for the data you posted, however I do not know if it will work for your entire vector.)

Try this:

X = string([0,301067677000000
0,314157576000000
0,327247475000000
0,340337374000000
0,353427273000000
0,366517172000000
0,379607071000000
0,392696970000000
0,405786869000000
0,418876768000000
0,431966667000000
0,445056566000000
0,458146465000000
0,471236364000000
0,484326263000000
0,497416162000000
0,510506061000000
0,523595960000000
0,536685859000000
0,549775758000000
0,562865657000000
0,575955556000000
0,589045455000000
0,602135354000000]);
Y = string([0,0193930890000000
0,0221282684000000
0,0245255339000000
0,0265479770000000
0,0281674962000000
0,0293643273000000
0,0301265743000000
0,0304497403000000
0,0303362581000000
0,0297950211000000
0,0288409140000000
0,0274943436000000
0,0257807698000000
0,0237302359000000
0,0213768998000000
0,0187585647000000
0,0159162099000000
0,0128935212000000
0,00973642228000000
0,00649260510000000
0,00321106079000000
-5,83896230000000e-05
-0,00326556436000000
-0,00636039014000000]);
V = sscanf(sprintf('%s.%s\n',X.'),'%f');
y = sscanf(sprintf('%s.%s\n',Y.'),'%f');
d2 = @(beta,alpha,N,phi,V) (4903985730770845.*N.*(((exp(alpha.*(phi - V.*(beta - 1))) + 1).*((alpha.^2.*beta.^2.*exp(alpha.*...
(phi - beta.*V)))./(exp(alpha.*(phi - V.*(beta - 1))) + 1) - (alpha.^2.*exp(alpha.*(phi - V.*(beta - 1))).*...
(exp(alpha.*(phi - beta.*V)) + 1).*(beta - 1).^2)./(exp(alpha.*(phi - V.*(beta - 1))) + 1).^2 + ...
(2.*alpha.^2.*exp(2.*alpha.*(phi - V.*(beta - 1))).*(exp(alpha.*(phi - beta.*V)) + 1).*(beta - 1).^2)./...
(exp(alpha.*(phi - V.*(beta - 1))) + 1).^3 - (2.*alpha.^2.*beta.*exp(alpha.*(phi - beta.*V)).*exp(alpha.*...
(phi - V.*(beta - 1))).*(beta - 1))./(exp(alpha.*(phi - V.*(beta - 1))) + 1).^2))./(alpha.*(exp(alpha.*(phi - beta.*V))...
 + 1)) + (exp(alpha.*(phi - V.*(beta - 1))).*(beta - 1).*((alpha.*beta.*exp(alpha.*(phi - beta.*V)))./(exp(alpha.*(phi -...
 V.*(beta - 1))) + 1) - (alpha.*exp(alpha.*(phi - V.*(beta - 1))).*(exp(alpha.*(phi - beta.*V)) + 1).*(beta - 1))./...
(exp(alpha.*(phi - V.*(beta - 1))) + 1).^2))./(exp(alpha.*(phi - beta.*V)) + 1) - (beta.*exp(alpha.*(phi - beta.*V)).*...
((alpha.*beta.*exp(alpha.*(phi - beta.*V)))./(exp(alpha.*(phi - V.*(beta - 1))) + 1) - (alpha.*exp(alpha.*(phi - V.*...
(beta - 1))).*(exp(alpha.*(phi - beta.*V)) + 1).*(beta - 1))./(exp(alpha.*(phi - V.*(beta - 1))) + 1).^2).*(exp(alpha.*...
(phi - V.*(beta - 1))) + 1))./(exp(alpha.*(phi - beta.*V)) + 1).^2))./63407003003326144512;
Ve = V(1:18);
ye = y(1:18);
ftns = @(b) norm(ye - d2(b(1),b(2),b(3),b(4),Ve));
PopSz = 500;
Parms = 4;
opts = optimoptions('ga', 'PopulationSize',PopSz, 'InitialPopulationMatrix',randi(1E+4,PopSz,Parms)*1E-2, 'MaxGenerations',2E3, 'PlotFcn',@gaplotbestf, 'PlotInterval',1);
t0 = clock;
fprintf('\nStart Time: %4d-%02d-%02d %02d:%02d:%07.4f\n', t0)
[B,fval,exitflag,output] = ga(ftns, Parms, [],[],[],[],[],[],[],[],opts)
t1 = clock;
fprintf('\nStop Time: %4d-%02d-%02d %02d:%02d:%07.4f\n', t1)
GA_Time = etime(t1,t0)
fprintf('\nElapsed Time: %23.15E\t\t%02d:%02d:%02d.%03d\n', GA_Time, hmsdv(GA_Time))
fprintf(1,'\tParameters:\n')
for k1 = 1:length(B)
    fprintf(1, '\t\tB(%d) = %8.5f\n', k1, B(k1))
end
xv = linspace(min(Ve), max(Ve), 250);
figure
plot(Ve, ye, 'p')
hold on
plot(xv, d2(B(1),B(2),B(3),B(4),xv), '-r')
grid

producing these parameter estimates:

	Parameters:
		B(1) = 15.96980
		B(2) =  0.63200
		B(3) = 542.18665
		B(4) =  7.56829

with the elements of ‘B’ being, in order: ‘beta’, ‘alpha’, ‘N’, and ‘phi’. They retain their full precision internally.

and this plot:

Plot images are not showing up with the ‘Picture’ icon for some reason, so I am also attaching the plot image as a separate file to be sure it is visible.

MATLAB: If condition returns no output

Replace this

if abs(DE)==1.1921e-07
disp(hj)
end

with this

if abs(DE)-1.1921e-07<1e-4
disp(hj)
end

Best Answer

Related Solutions

MATLAB: Curve fitting by Genetic Algorithm

MATLAB: If condition returns no output

Related Question