MATLAB Neural Network Classification Problem

[lkh1986]

Homework Statement

P = [data(1:65,11:100) data(1:65,411:500)]; % Input, all together 180 data set, 65 dimension

T = [ones(1,90) zeros(1,90);
zeros(1,90) ones(1,90)]; % Actual output, first 90 data set belong to class I and the next 90 belong to class II

net = newrb(P,T); % Train networkPnew = [data(1:65,1:10) data(1:65,401:410)]; % Data set for testing, 20 data set, first 10 belong to class I, next 10 belong to class II

Y = sim(net,Pnew);

Homework Equations

The Attempt at a Solution

The desired output, Y should be
[1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0;
0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1];

However, all I get is Y =
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1;
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ];

Hm, anything wrong with the code? Thanks.

 

[jonson074]

Hi friends

I have a problem,

I have two sets of data of aircraft engine, first is the normal data which have been recorded by sensors and the other one is the abnormal data (when the engine is faulty): Now I have been asked :
1-to model of normality (based on normal data ) using Neural Network in order to classify the faulty and healthy class of the system .

2-and model of abnormality (based on abnormal data) using Neural network , this time I have to test data against model of normality+model of abnomality such that when I tested data by NN , if the data looks like normal model so should be in the normal class ,if looks like abnormal class , so it is abnormal class..

Please help me as I am new in the nueral network (please someone guide me by MATLAB code) please please...

Thanks