I created a subsystem in Simulink with mask underneath. There are all sorts of control and calculation inside this subsystem. Now I have to duplicate this subsystem for one hundred thousand times because I need to connect one hundred thousands of this block in series.
What I have tried, I used the commands “add_block” and “add_line” where I can just type it in the Matlab command and the blocks and lines are added automatically.
What I wish to do now is,
I want to have 100 signals in a single subsystem, so instead of using one hundred thousand subsystem, I will only need one thousand of this subsystem, I understand that this can be done by vectorization.
I have a very limited knowledge on using vectorization feature in Matlab/Simulink. I appreciate if anyone of you could provide me a great reference on how to do this?
What I found here is something like this which I could not link it to my issue above: http://www.mathworks.co.uk/help/matlab/matlab_prog/vectorization.html
The other thing I found is by "using vectorization for most components. Most components are vectorized if they have a vectorized input signal or if one of their parameter is specified as a vector."
However, I could not find any further information/details, appreciate if anyone of you could give opinion on this? Thanks!
Related
I want to integrate a Modelica variable over time, just for convenience in plotting and post-processing. The variable I want to integrate over time is the power of a compressor so that I get the total energy. The first idea would be to add these lines:
Modelica.Units.SI.Power P_comp;
Modelica.Units.SI.Energy E_comp;
equation
P_comp = der(E_comp);
Is that the recommended way, or are there (better?) alternatives? Is it expected to influence the selection of dynamic states?
Assuming that those two lines are the only ones using E_comp that should work.
Basically E_comp will be part of its own separate state-selection block and changes there shouldn't influence anything else.
However, state selection consists of a number of algorithms and heuristics so it is difficult to formally guarantee that any change does not influence it.
I could imagine some strange possibilities that would break this, but I don't think anyone has implemented them - and I don't see a use-case for them (except to mess up cases like this).
And if you instead of integrating want to differentiate a signal it is a lot messier.
I know Matlab has the function TrainAutoencoder(input, settings) to create and train an autoencoder. The result is capable of running the two functions of "Encode" and "Decode".
But this is only applicable to the case of normal autoencoders. What if you want to have a denoising autoencoder? I searched and found some sample codes, where they used the "Network" function to convert the autoencoder to a normal network and then Train(network, noisyInput, smoothOutput)like a denoising autoencoder.
But there are multiple missing parts:
How to use this new network object to "encode" new data points? it doesn't support the encode().
How to get the "latent" variables to the features, out of this "network'?
I appreciate if anyone could help me resolve this issue.
Thanks,
-Moein
At present (2019a), MATALAB does not permit users to add layers manually in autoencoder. If you want to build up your own, you will have start from the scratch by using layers provided by MATLAB;
In order to to use TrainNetwork(...) to train your model, you will have you find out a way to insert your data into an object called imDatastore. The difficulty for autoencoder's data is that there is NO label, which is required by imDatastore, hence you will have to find out a smart way to avoid it--essentially you are to deal with a so-called OCC (One Class Classification) problem.
https://www.mathworks.com/help/matlab/ref/matlab.io.datastore.imagedatastore.html
Use activations(...) to dump outputs from intermediate (hidden) layers
https://www.mathworks.com/help/deeplearning/ref/activations.html?searchHighlight=activations&s_tid=doc_srchtitle
I swang between using MATLAB and Python (Keras) for deep learning for a couple of weeks, eventually I chose the latter, albeit I am a long-term and loyal user to MATLAB and a rookie to Python. My two cents are that there are too many restrictions in the former regarding deep learning.
Good luck.:-)
If you 'simulation' means prediction/inference, simply use activations(...) to dump outputs from any intermediate (hidden) layers as I mentioned earlier so that you can check them.
Another way is that you construct an identical network but with the encoding part only, copy your trained parameters into it, and feed your simulated signals.
I have data sets for two groups, with one being much smaller than the other. For that reason, I am using the MatLab bootstrapping function to estimate the performance of the smaller group. I have code that draws on my original data, and it generates 1000 'new' means. However, it is not clear as to how many of the original data points are used each time. Obviously, if all the original data was used, the same mean would continue to be generated.
Can anyone help me out with this?
Bootstrapping comes from sampling with replacement. You'll use the same number of points as the original data, but some of them will be repeated. There are some variants of bootstrapping which work slightly differently, however. See https://en.wikipedia.org/wiki/Bootstrapping_(statistics).
Some general Modelica advice?
We've built a model with ~2000 equations and three vectors of input from measured data. Using OpenModelica, attempts at simulation have begun to hang in the translation stage (which runs for hours where it used to take less than a minute) and now I regularly "lose connection to omc.exe." Is there perhaps something cumulative occurring that's degrading translation/compilation performance?
In general, are there any good rules of thumb for keeping simulations lighter and faster? I realize that, depending on the couplings, additional equations could be exponentially increasing the size of the resulting system of equations - could this be a problem?
Thanks for your thoughts!
It shouldn't take that long. Seems like a bug.
You can report this bug here:
https://trac.openmodelica.org/OpenModelica (New Ticket).
If your model is public you can post it there, if not you can contact the OpenModelica team privately.
I did some cleaning in the code; and got the part that repeats 12x (the module) down to ~180 equations; in the process I reduced the size of my input vectors (and also a 2D look-up table the module refers to) by quite a bit - they're both down to a few hundred values. It's working now--simulations run in reasonable time, a few minutes each.
Since all these tables were defined within Modelica functions (as you pointed out, Mr. Tiller) perhaps shrinking them helped to improve the performance. I had assumed that all that data just got spread out in a memory array, without going through any real processing, but maybe that's not the case...time to know more about what's going on under the hood in this environment (as always).
Thanks for the help!
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.