MATLAB: Back Propagation Accuracy improvement

back propagation neural network

I have used BPN to classify my SAR images.I have done this work and finally performed accuracy also.i got 90% of accuracy. To perform this accuracy i used true positive,true negative,false positive and false negative concept. I have given my code which i used for BPN classification.
A =[5.339 6.1692 5.9526 10.1191 2.3506 0.8338]*100;( this is my image pixel value i performed pixel based classification) x =[0 1 2 3 4 5]; net1 = newff(minmax(A),[60,6,1],{'tansig','tansig','purelin'},'trainrp'); net1.trainParam.show = 1000; net1.trainParam.lr = 0.04; net1.trainParam.epochs = 700; net1.trainParam.goal = 1e-5; [netan] = train(net1,A,x); save abc y = round(abs((sim(netan,A))))
can anyone Please tell me how to improve the accuracy of neural network.
Thank you

Best Answer

Why aren't you using the classification nets patternnet(special form of feedforwardnet) or newpr (special form of newff) with one hidden layer?
Typically, the only thing you have to specify is the number of hidden nodes.
Search the NEWSGROUP and ANSWERS for my classification/pattern-recognition posts.
 Hope this helps.
Thank you for formerly accepting my answer
Greg
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