Sir, I need to use RBF NN for a classification problem. My input is 8*646 and target is 1*646. My aim is out of 8 features which i am giving as input , network should classify whether it belongs to class A or class B (Using same data set for testing also).Out of 646 data, first 233 belongs to class A and rest belongs to class B. So i give target as a row vector like: zero’s for the first 233 & ones for rest. [0,0,0…….(233nos.),1,1,1…….] (1)Is it the right way? Then i used newrb like this: Net=newrb (P,T,eg,sc); And used the same input to simulate the network and plotted the confusion matrix. Y=net(P); plotconfusion(T,Y).
(2)How to set this eg and sc values? Means how can i find the optimal values of eg and sc for my problem?? SETTING EG=.02 and sc=0.1,When i plot confusion it is giving 98% correct classification and output Y I got correctly one for class B and 0.0528 for class A. (3) How can i get exact zeros for class A??? (4) How can i use LOGSIG transfer fn in second layer instead of PURELIN???? I tried rbf with nntool also. We will get output and errors after simulation. (5) What does it mean????? And same as question (3) in o/p class A is not zeros but Class B is ones. Why is it so? Later i need to include more type of classes and for a given set of same features network should tell in which group it belongs to. (6) For my problem which one u think is more helpful- RBF EXACT FIT OR RBF FEWER NEURONS??????
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