MATLAB: Clarification for parallel process

MATLABparfor

I have set up one mainScript, where one parallel for initiates calling of func1 function. This function calls script1 within. This means at a time several for loop is accessing script1 (though through a function). Can these be a problem for variables?

mainScript
  func1
    script1

Best Answer

No, every function call has its own workspace, they do not interfere with each other unless you try to do it yourself (for example using evalin(), assignin() etc.).

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MATLAB: Need some clarification

Just from looking at the code, it looks as though you're dividing a scalar by a column vector:

y = data(:,1);
...
...x(3)/(y.^2)...

This will result in a matrix right-divide, which will return a row vector. But

x(2).*(y.^2)

will give a column vector. And adding these together, of course, gives our good friend "Matrix dimensions must agree".

I suspect you want to do ./ instead of /.

MATLAB: Some clarifications on ANN

PLEASE DO NOT START A NEW THREAD WITH FOLLOWUP QUESTIONS TO ANOTHER THREAD.

> Could you please clarify the answer once again. I did not understand it.

OK:

1. There are only 2 layers 1 hidden and 1 output

2. The challenge is to choose the smallest number of hidden nodes that will yield satisfactory results.

3. For straightforward training you would like many more output training equations than unknown weights in order to mitigate errors caused by noise, measurement error, etc.

4. If this is not possible, validation stopping and regularized training can be used. The former is the NNTBX default.

> For training say 52 vectors , it could also be 100 vectors how to decide how many layers of perceptrons should I use for effective training in the following function? net=newff(final,target,9).

CORRECTION OF TERMINOLOGY AND MISUNDERSTANDING:

1. Perceptron is the name of a TYPE of network.

2. The standard multilayer perceptron (MLP) has two layers: 1 hidden and 1 output

3. Given the dimensions of input and output vectors (I and O), the main differences between standard MLPs is H, the number of hidden nodes and the values of the corresponding Nw = (I+1)*(H+1)*O connection weights.

4. With Ntrn training vector pairs, there are Ntrneq = Ntrn*O training equations.

5. If you do not use validation stopping or regularized training (MSEREG) it is desirable to have Ntreq >> Nw. Otherwise, H can be larger.

>Please give me a clarification about is there any ration to be maintained between the no. of perceptrons (CORRECTION: HIDDEN NODES) and the no of training vectors.

6. See 5.

> Thanks in advance for your help.

Hope this helps.

Thank you for formally accepting my answer.

Greg

Best Answer

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