Hi Chase
thanks for the email. thanks for the tip on use of logistical classifiers.
Agreed the PID (and variations ) is a seemingly perfect fit , at least
at the top level.. My guess is that the type of disturbance the 'the
system' (affecting, ultimately, the set temperature) (the device) could
be classified (in real time) as a cause of different mechanisms, and for
a specific mechanism, there might be a more optimal solution to minimize
error.
My primary intrest in these things looking at new ways to do old things
better.. I like systems that predict the error that is coming, before
it occurs...so I like adaptive filter driven control systems . I am
slowly getting my head around alpha-beta and Kalmans as time permits.
The most popular neural net function is of course computers playing
games- feed it the history of 10,000 games and as Chase says, it figures
out the patterns of Y in a sea of X
If anyone is interested in this stuff, you dont need to buy a dev kit.
You can do it all in Python. Or C . Once you understand the basics , it
is easy enough to program. If you dont understand the basics, you might
not be able to acheive a desired outcome.
There are quite a few good books on these subjects for Python for those
interested.
I wish I could go back to school and do a year or two on this stuff...
glen
On 10/07/2019 12:23 PM, Chase Turner wrote:
Hi Glen,
This is actually something I know a little about.
Neural nets are most useful for feature selection, that is, finding the
important x that is a function of y, in a very large sea of x variables. In
this case, we already know what's important, which is temperature
stability. So, a neural net would be a bit much when we already know what
feature is important for function. Additionally, unless I'm mistaken, oven
control is probably a linear relationship of some sort or another, and
neural nets are much better suited for examining and revealing insights
about non-linear data.
If you have a method by which you can collect the necessary data that has a
bearing on the oven functionality, you'd probably be better off training a
logistic classifier, and using it instead. That said, both methods would be
overkill, imo- I'd use a PID instead.
Best,
Chase
On Tue, Jul 9, 2019 at 10:00 PM Glen English VK1XX <
glenl...@pacificmedia.com.au> wrote:
Has anyone tried to use a Neural net to control oven tmep, rather than
the ye olde PID ?
IE the algorithm learns from previous beheviour and successfully
predicts behaviour (or not).
I'm sure there are a few out there proficient with machine learning
algorithms.
Might make a good masters thesis I bet.
Given that oven control based on inputs and whatever is not random,
unlike say flicker etc.
glen
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