probabilistic neural network coding problem
1 Ansicht (letzte 30 Tage)
Ältere Kommentare anzeigen
abdulhamid
am 16 Dez. 2013
Beantwortet: Greg Heath
am 17 Dez. 2013
i have create this simple code and trying to make classification between two inputs, but, after run the code the output is not overlapping the input and is not fitting the target, even the output it self i am not sure is looks like ANN output? or PNN has different output functions.
any one can give suggestion please.
regards
code is here:
clc
format short
p=[1.7572 8.3747 6.6941 4.0114 1.7572 1.9934 4.9658 8.4807 5.3349 5.5587 2.7213 1.9934 8.8202 4.9658 5.754 7.809 4.3451 4.7672
1.30E+08 5.81E+05 -6.63E+05 3.47E+06 1.30E+08 1.19E+08 2.68E+08 -2.39E+05 4.99E+05 -9.24E+04 -5.09E+06 1.19E+08 -7.06E+04 2.68E+08 -3.67E+05 -2.80E+05 -8.74E+05 -1.77E+05
-0.3045 -0.4317 -0.8981 0.1139 -0.3045 -0.0431 1.6113 -0.5305 -0.4704 -0.0686 -0.0464 -0.431 -0.4884 1.6113 -0.4654 -0.1465 -0.6266 0.6488
5.02E+07 2.09E+07 7.13E+06 2.48E+07 5.02E+07 4.92E+07 1.71E+08 7.47E+06 7.14E+06 7.85E+06 5.50E+07 4.92E+07 7.06E+06 1.71E+08 7.56E+06 2.96E+07 1.14E+06 1.17E+07
];
t=[0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0
];
plottools('on');
spread= 10;
net=newpnn(p,t,spread);
net.performFcn;
toc;
tic;
plottools('on');
hold on;
%net.trainparam.show=1;
nettrainparam.epochs=1000;
%net.trainparam.goal=0.0002;
%net.trainParam.searchFcn = 'srchbac';
nettrainparam.lr=0.5;
%net=train(net,p,t);
y=sim(net,p);
plotregression(t,y);
%plottraining (t,y);
plottools('on');
error=abs(y-t);
plot(error);
%y=sim(net,p);
grid on;
legend off;
plottools('on');
%plot(p,t,'*');
%e = t-y
% perf = mse(e);
% perf = sse(e);
End
0 Kommentare
Akzeptierte Antwort
Greg Heath
am 17 Dez. 2013
1. Class 1 has 2 duplicate points and class 2 has 1 duplicate point
2. There is an 8 orders of magnitude difference between variables (1,3) and variables (2,4). Therefore, I standardized the data to have zero mean and unit variance.
3. Using newpnn with 0 < spread < 2 I was not able to separate the classes.
I recommend you use newrb and loop over spread values to obtain the best result.
Thank you for formally accepting my answer
Greg
0 Kommentare
Weitere Antworten (0)
Siehe auch
Kategorien
Mehr zu Define Shallow Neural Network Architectures finden Sie in Help Center und File Exchange
Produkte
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!