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Re: RBFNN
Posted:
Dec 6, 2012 7:12 PM


"dean86" wrote in message <k9klgr$lss$1@newscl01ah.mathworks.com>... > "Greg Heath" <heath@alumni.brown.edu> wrote in message <k9jc90$905$1@newscl01ah.mathworks.com>... > > "dean86" wrote in message <k9id39$82r$1@newscl01ah.mathworks.com>... > > > Hi all, > > > I have the following problem: > > > 777 input from some different sensors, I have to do a PCA and then a RBF to predict the number of analytes. > > > > What type of sensors? Unfortunately the trace that I have doesn't specify the type of sensors, it says just "Different kind of sensors". > > > How many measurement vectors for NN input? N = 343? Yes > > > How many dimensions in each input vector? I = 777 ? Yes > > > Regression or classification ? I'm sorry but actually I just know that I have to use the scores of the PCA to train a radial basis function neural network to predict concentration of the analytes. I'm sorry but once again the web is the only resource that I have to learn this topic. > > > What is the output ? > > How many dimensions in each output vector? O = ? > > > > > Could you help me to understand the correct way to do the RBF, to use the command 'newrb', but above all, how to understand the results? > > > > > First thing that I do is to load the data, then I have this matrix 777x343, so I do the transpose and I start to do the meancentring and then the PCA on this matrix and I obtain the scores (343x4) and the loadings (777x4). Now I have to use this scores to do this RBF, so I obtain the transpose of the scores matrix (4x343) and now, should I use the newrb with this last matrix and the original data matrix (777x343)? > > > > > What is the criterion for input dimensionality reduction why 777==> 4? This makes no sense to me. Because when I use the PCA I can clearly see from the plot that I can keep just 4 variables. > > > > All NEWRB needs are input and output matrices and a reasonable value for the MSE goal and a range of candidate spread values. > > > > For regression standarize both input and target matrices. For cclass classification, standardize the input matrix but use one of c (=O) binary coding for the output matrix. > > > > Use MSEgoal = 0.01*mean(var(t',1)) % yields R^2 >= 0.99 > > > > Obtain multiple designs from a loop over spread values. I usually start with a coarse search spread = 2^(i1), i = 1,2,... Then refine the search if needed. > > > > Old posts: > > > > 5 threads for heath newrb overfitting overtraining > > > > Neural Networks Question > > Newrb with kmeans training > > *RBFNN Design using MATLAB's NEWRB > > Retrain the created neural network > > *Training Feed Forward Neural Networks > > > > 3 threads for heath newrb overfitting overtraining > > > > Question Regarding RBF? > > Neural Network  Incremental Training > > train rfb newrb > > > > 2 threads for heath newrb overfitting overtraining > > See "*" above What is the size of your output target matrix?
Greg Greg



