Discover MakerZone

MATLAB and Simulink resources for Arduino, LEGO, and Raspberry Pi

Learn more

Discover what MATLAB® can do for your career.

Opportunities for recent engineering grads.

Apply Today

Thread Subject:
RBFNN

Subject: RBFNN

From: dean86

Date: 3 Dec, 2012 14:31:05

Message: 1 of 5

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.
Could you help me to understand the correct way to do the RBF, to use the command 'newrb', but above all, how to undestand 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)?

Thanks in advance to everyone

Subject: RBFNN

From: Greg Heath

Date: 3 Dec, 2012 23:23:12

Message: 2 of 5

"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?
How many measurement vectors for NN input? N = 343?
How many dimensions in each input vector? I = 777 ?
Regression or classification ?
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.

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

Hope this helps.

Greg

Subject: RBFNN

From: dean86

Date: 4 Dec, 2012 11:07:07

Message: 3 of 5

"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
>
> Hope this helps.
>
> Greg

Subject: RBFNN

From: Greg Heath

Date: 7 Dec, 2012 00:12:10

Message: 4 of 5

"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

Subject: RBFNN

From: dean86

Date: 7 Dec, 2012 20:14:09

Message: 5 of 5

"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

Tags for this Thread

What are tags?

A tag is like a keyword or category label associated with each thread. Tags make it easier for you to find threads of interest.

Anyone can tag a thread. Tags are public and visible to everyone.

Contact us