On 11/1/2014 6:03 PM, Hal Murray wrote:
dan-timen...@drown.org said:
I'm experimenting with using a temperature sensor to estimate local
oscillator frequency changes. My goal is to have a decent holdover clock
for a NTP server with not so great GPS antenna placement.
This is for ntpd rather
Hi
This sort of thing is normally done with a precisely controlled temperature
chamber and multi day temperature ramp runs. Even then there is a bit of
“wonder what that was, let’s try it again”.
If you are looking at a crystal oscillator, what you have is a perturbation in
the frequency /
Ok, I hadn't considered rate of change. This data is currently 3
day's worth and seems to repeat itself on both days at the same
temperature point. Attached is a time based graph to show that. The
ppm axis (on the right) is inverted to make it easier to see the
relationship between the
I gave zunzun a try and the one with the lowest root mean squared error was:
f(x) = a( x**0.5) + b( x ) + c( sin(x) ) + d( cos(x) )
It got 0.202 RMSE, so I guess I'll stick with my original function as
it seems to be closer to what I expect will happen at colder/hotter
temps.
You have a
Hi
On Nov 1, 2014, at 5:09 PM, Dan Drown dan-timen...@drown.org wrote:
Ok, I hadn't considered rate of change.
It’s one of the limits on this sort of thing in general. It’s even more of an
issue with a coupled mode like the one you show. Since there are an enormous
number of possible
dan-timen...@drown.org said:
I'm experimenting with using a temperature sensor to estimate local
oscillator frequency changes. My goal is to have a decent holdover clock
for a NTP server with not so great GPS antenna placement.
This is for ntpd rather than chrony, but it's a good read:
If this is just for holdover then I don't think you even want a general
solution. Have the controller always keep the last few days of data for
temperature vs. EFC value. Then if GPS fails use the most recent data for
the current temperature. This makes for a self adapting system accounting
for
-nuts] Digital temperature compensation
I gave zunzun a try and the one with the lowest root mean squared error
was:
f(x) = a( x**0.5) + b( x ) + c( sin(x) ) + d( cos(x) )
It got 0.202 RMSE, so I guess I'll stick with my original function as it
seems to be closer to what I expect will happen
I think you have a good point - any model is going to have a larger
error than the data itself. I'll be looking into this.
Quoting Chris Albertson albertson.ch...@gmail.com:
If this is just for holdover then I don't think you even want a general
solution. Have the controller always keep