Date: Dec 6, 2012 7:12 PM Author: Greg Heath Subject: Re: RBFNN "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 mean-centring 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 c-class 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^(i-1), i = 1,2,... Then refine the search if needed.

> >

> > Old posts:

> >

> > 5 threads for heath newrb overfitting overtraining

> >

> > Neural Networks Question

> > Newrb with k-means 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