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dean86
Posts:
9
Registered:
11/22/12
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Re: RBFNN
Posted:
Dec 7, 2012 3:14 PM
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"Greg Heath" <heath@alumni.brown.edu> wrote in message <k9rc8q$61e$1@newscl01ah.mathworks.com>...
> "dean86" wrote in message <k9klgr$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?
1x343
> Greg
>
> Greg
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