Jeff Goslin writes:
>Actually, the data is going to be used to train a neural
>net to simulate the operation of a pancreas.
>I use artificial intelligence methods to solve complex
>problems that normally require a very complex equation
>or human intelligence to resolve. I would *LIKE* to
>remove humans from the equation and attempt to create
>an intelligence to perform the task of the pancreas.
>For the INITIAL runs, I don't need absolutely perfect data.
Many of us on the list are skeptical about artificial intelligence methods.
They have proven useful for some applications, but they are often marketed
as if they can substitute for consultation with a professional statistician.
Putting aside that skepticism, have you thought about how you are going to
measure how successful your neural net is at mimicking a human pancreas? In
particular, you do not seem to have data on insulin production, so how will
you know that the neural net pancreas produces insulin at a rate comparable
to the human pancreas?
If you can quantify a measure of success, it might help us suggest what sort
of interpolation you need. I suspect that you do not really want or need
interpolation, because the interpolated values will not add any independent
information to the training set above and beyond the actual data values.
Think about it. The neural net uses weighted input to train itself. The
interpolated values are weighted from the same input. So wouldn't the
interpolated values be redundant?
You might want to try training your net with just the actual data, and with
some very simple interpolation, such as a straight line interpolation
between adjacent points. Does the net with the interpolated values perform
better than the net with just the original data?
You might also want to think about what is known about insulin production.
If it has cycles that are more frequent than every two hours, then any
attempt at extrapolation will have to miss those cycles, wouldn't it?
Try not to get too impatient with our questions. It is perhaps an attempt to
get you to elaborate more about the fundamental nature of the problem.
Also, forgive me if my questions are a bit naive. It is hard to discuss
complex issues via email.
Steve Simon, [EMAIL PROTECTED], Standard Disclaimer.
STATS: STeve's Attempt to Teach Statistics. http://www.cmh.edu/stats
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