Is there a way to stop a run in Netlogo when the system reaches equilibrium? The ticks just keep running and I would like my simulation to automatically stop when a near-equilibrium change is reached, i.e. when the system is close to 95% equilibrium.
I have a similar situation where I've taken two approaches. When there is a variable for which stability indicates quasi-equilibrium, I keep a history of that variable in a list and check the standard deviation of the most recent "n" values. If that variance falls below a given value, I stop the simulation. For you, that might be concentration. Of course if the variable is moving smoothly in one direction or another you might get a false positive, so as a check, I've also regressed the most recent values to see if the slope is "close" to zero. I'd be hard put to put a percentage on closeness- that will depend on the situation. But, something like this might do.
Hope this helps,
Charles
Related
I am hoping somebody here will be able to help me out with my small issue with one of the Simulink/Matlab code. It is quite similar to the problem that I’ve discussed earlier, but a little bit more complicated and now it is more a Simulink issue, rather than a Matlab one.
So I have a turbine which speed is controlled by the gate’s opening, hence the control voltage. By controlling the gate opening I am accelerating the turbine and at some point in time, I need to introduce a saturation effect (since I am testing the code now, it will be done an external signal). This effect won’t change the control voltage, but it affects other components of the system, hence at the same control voltage, the turbine’s speed will go up. But at the same time, I need to keep the speed at the same value as it was before the saturation effect (let’s say it was 320 rpm). To do so I need to decrease the control voltage and should keep doing it until I reach the speed as it was before. There is no need to do it instantly (this approach will be later introduced in hardware), but it will be a nice thing to check the algorithm in these synthetic tests.
In terms of the model, I was planning to use a while loop with the speed requirement “if speed > 320” again, now just to simplify things. To decrease the control voltage I was planning to subtract from the original 50 (% opening) - 0.25 (u2) at first and after that increasing this value by 0.25 until I decrease the speed below 320. I can’t know the exact opening when this requirement will be satisfied, hence I need some kind of algorithm to “track” this voltage.
So it should be something like this:
u2 = 0;
While speed > 320
u2 = u2+0.25
End
u2 is initially zero since we have a predefined initial control voltage. And obviously, when we reach the motor’s speed below 320, I need to keep the latest value of the u2 (and control voltage).
Overall, it is a small code and should be done in Simulink (don’t want to introduce any other Fcn function into the model). I’ve never used while and if blocks in Simulink, but so far I came up with this system. It’s a simplified version of my model, but the control principle is the same.
We are getting the motor speed of 350, compared with 320 (the speed before “saturation), and if our speed after saturation is higher, we need to reduce the control voltage. To trigger the while loop block I’ve decided to use a simple switch. The while block meanwhile is:
Definitely not the best implementation but I was trying a lot of different combinations and without any real success. I am always getting the same error:
Was trying to use a step signal instead of the constant “7” – to model acceleration of the motor, and was getting the same error at the moment of acceleration above 320 threshold. So looks like the approach is almost right but mathematically it fails to find the most suitable solution. I’ve tried to implement a transport delay in the memory part of the while subsystem but was getting errors during compilation all the time.
Are there any obvious (and not so) mistakes? Or maybe from the beginning, I should have chosen another approach… I really hope that somebody will be able to help. Thank you in advance and have a great day.
I do not think that you have used While block correctly.
This is what I have done, I used a "Matlab function" block instead of "While" block as follows,
The function in Matlab function is
function u2=fcn(speed,u2d)
if speed>320
u2=u2d+0.25;
else
u2=u2d;
end
And the results I have got, Scope 1
Scope
Edit
As you prefer a function free model, the following may do the same.
I know it sounds strange and that's a bad way to write a question,but let me show you this odd behavior.
as you can see this signal, r5, is nice and clean. exactly what I expected from my simulation.
now look at this:
this is EXACTLY the same simulation,the only difference is that the filter is now not connected. I tried for hours to find a reason,but it seems like a bug.
This is my file, you can test it yourself disconnecting the filter.
----edited.
Tried it with simulink 2014 and on friend's 2013,on two different computers...if Someone can test it on 2015 it would be great.
(attaching the filter to any other r,r1-r4 included ''fixes'' the noise (on ALL r1-r8),I tried putting it on other signals but the noise won't go away).
the expected result is exactly the smooth one, this file showed to be quite robust on other simulations (so I guess the math inside the blocks is good) and this case happens only with one of the two''link number'' (one input on the top left) set to 4,even if a small noise appears with one ''link number'' set to 3.
thanks in advance for any help.
It seems to me that the only thing the filter could affect is the time step used in the integration, assuming you are using a dynamic time step (which is the default). So, my guess is that (if this is not a bug) your system is numerically unstable/chaotic. It could also be related to noise, caused by differentiation. Differentiating noise over a smaller time step mostly makes things even worse.
Solvers such as ode23 and ode45 use a dynamic time step. ode23 compares a second and third order integration and selects the third one if the difference between the two is not too big. If the difference is too big, it does another calculation with a smaller timestep. ode45 does the same with a fourth and fifth order calculation, more accurate, but more sensitive. Instabilities can occur if a smaller time step makes things worse, which could occur if you differentiate noise.
To overcome the problem, try using a fixed time step, change your precision/solver, or better: avoid differentiation, use some type of state estimator to obtain derivatives or calculate analytically.
I'm writing code, that executes MLE. At each step, I get gradient at one point and then move along it to another point. But I have problem with determination of magnitude of the move. How to determine the best magnitude for good convergence? Can you give me an advice how to avoid other pitfalls, such as presence of several maximums?
Regarding the presence of several maxima: this issue will occur when dealing with a function that is not convex. It can be partially solved by multi-start optimization, which essentially means that you run the simulation multiple times in order to find as many maxima as possible and then selecting the 'highest' maximum from among them. Note that this does not guarantee global optimality, as the global optimum might be hard to reach (i.e. the local optima have a larger domain of attraction).
Regarding the optimal step size for convergence: you might want to look at back-tracking linesearch. A short explanation of it can be found in the answer to this question
We might be able to give you more specific help if you could give us some code to look at, as jkalden already pointed out.
I am new with signal processing, i have following signals which i've got after some pre-processing on original signals.
You can see some of them has some similarities with others and some doesn't. but the problem is They have various range(in this example from 1000 to 3000).
Question
How can i analysis their properties scale-free(what i mean from properties is statistical properties of signals or whatever)??
Note that i don't want to cross-comparing the signals, i just want independent signals signatures which i can run some process on them sometime later.
Anything would help.
If you want to make a filter that separates signals that follow this pattern from signals that don't, well, there's tons of things you could do!
Just think practically. As a first shot at it, you could do something like this (in this order):
Check if the signals are all-positive
Check if the first element is close in value to the last element
Check if the maximum lies "in the middle" somewhere
Check if the first value is small, then the signal grows, then shrinks again
Check if the growth rates are gradual. You could for example analyze their derivatives (after smoothing):
a. derivative should be all-positive for a while, then all-negative.
b. derivative should be smooth (no jumps greater than some tolerance)
Without additional knowledge about the signal's nature/origin, it's going to be hard to come up with more meaningful metrics than these...
I have some animations happening upon fine acceleration detections. But when the user sits in a car or is walking it may get annoying.
Basically, all that stuff has to be disabled automatically as soon as there is too much vibration or shaking. Conceptually, I think that it's very hard to filter those vibrations out , since the "vibration phase" changes permanently. I woul define "unwanted vibration or shocks" as acceleration values that change very fast by an large interval of values, or, an permanently changing accumulated value that does not exceed an specified treshold range in an specified minimum period of time.
I am looking for "proven" concepts, before I start reinventing the wheel for a couple of days.
I don't have any concrete answers for you, but you might want to Google band-pass filters or anti-aliasing filters for some ideas on how to approach this. Basically, if you can identify the frequency range of accelerations that you want to consider real, you can filter out frequencies that fall outside this range.
Before you start doing too much pre-optimization, I think you should implement a low pass filter and see if that does the job. Most iPhone apps effectively use a variation of an LPF to get rid of unwanted accelerometer noise.
You could also go the other way and use a high pass filter. Once you get a certain power level passing through the HPF, stop processing data.