Hi all,
I have two (2) sensor datasets (FRF datasets from two (2) accelerometers positioned at different locations), each dataset is of 6540 observations and 4000 features. I want to use a Neural Network for pattern recognition to classify eleven (11) conditions (1 fault-free case + 10 faulty cases).
I managed to get excellent results using one (1) dataset from one (1) sensor using this code:
% Creating the target matrices
% ti refers to the target matrix for the ith case. bi refers to the number of observations for the ith case.
t1 = zeros(11,b1); t1(1,:) = ones(1,b1); t2 = zeros(11,b2); t2(2,:) = ones(1,b2); t3 = zeros(11,b3); t3(3,:) = ones(1,b3);
t4 = zeros(11,b4); t4(4,:) = ones(1,b4); t5 = zeros(11,b5); t5(5,:) = ones(1,b5); t6 = zeros(11,b6); t6(6,:) = ones(1,b6);
t7 = zeros(11,b7); t7(7,:) = ones(1,b7); t8 = zeros(11,b8); t8(8,:) = ones(1,b8); t9 = zeros(11,b9); t9(9,:) = ones(1,b9);
t10 = zeros(11,b10); t10(10,:) = ones(1,b10); t11 = zeros(11,b11); t11(11,:) = ones(1,b11);
TARGETS = [t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11]; % The overall target 11*6540 matrix
INPUTS = Dataset; % The overall 4000*6540 dataset matrix. 4000 features and 6540 observations.
net = patternnet(20); [net,tr] = train(net,INPUTS,TARGETS); plotperform(tr)
testX = INPUTS(:,tr.testInd); testT = TARGETS(:,tr.testInd); testY = net(testX); testIndices = vec2ind(testY);
plotconfusion(testT,testY)
[c,cm] = confusion(testT,testY)
fprintf('Percentage Correct Classification : %f%%\n', 100*(1-c)); fprintf('Percentage Incorrect Classification : %f%%\n', 100*c);
plotroc(testT,testY)
Now, to use both datasets (from 2 sensors), I'm considering following the same method mentioned above for both datasets and then concatenate the relavent matrices creating one (4000*13080) INPUTS matrix and one (11*13080) TARGET matrix before applying the NN model.
Is there a better way? Please help, and thanks in advance!
