(Original post: http://stackoverflow.com/questions/26149416/using-machine-learning-to-predict-the-collapse-stabilization-of-complex-system)

I should add that I've also come up since this stackoverflow post with various methods ranging from Windowing, to convolution, FFTs, using a variation of expansion meshing, and even a method that revolved around trying to perform a Monte Carlo tree search over a set of systematic samples (really overly complicated, didn't work).

I'm at a serious loss of ideas right now. This is a project that I'm doing with a 5 University students, and all of us are clueless on how to correctly approach this.

If anyone has an idea on how to approach this, please let me know.

My guess is that the typical SO reader has no idea what you're trying to do. For example the below is completely meaningless to me:

I've been working on a fuzzy logic SDK for the past 3 months now, and its come to the point where I need to start heavily optimizing the engine.

As with most "utility" or "needs" based AI systems, my code works by placing various advertisements around the world, comparing said advertisements against the attributes of various agents, and "scoring" the advertisement [on a per agent basis] accordingly.

It's not clear to me which terms you're using as AI terms and which terms are problem domain specific. What does placing advertisements around the world have to do with a fuzzy logic SDK?

Perhaps if we understood the problem domain better, the solution could avoid machine-learning to detect expected crash conditions.

I think I don't need to find out which one is going to be the butterfly, I think I only need to find out when a collapse will happen.

I've been asking various professors and math grad students. I've gotten various suggest from using Neural networks, to multivariate Markov chains. Any opinions?

Markov chain description VIA reddit:

For the non-periodic repetition you might be able to capture it using a multivariate Markov chain. It won't reproduce the exact behavior but it should be able to approximate it. You might want to do some sensitivity analysis to see how robust your system is to approximations of the attributes.

My guess is that the typical SO reader has no idea what you're trying to do. For example the below is completely meaningless to me:

I've been working on a fuzzy logic SDK for the past 3 months now, and its come to the point where I need to start heavily optimizing the engine.

As with most "utility" or "needs" based AI systems, my code works by placing various advertisements around the world, comparing said advertisements against the attributes of various agents, and "scoring" the advertisement [on a per agent basis] accordingly.

It's not clear to me which terms you're using as AI terms and which terms are problem domain specific. What does placing advertisements around the world have to do with a fuzzy logic SDK?

Perhaps if we understood the problem domain better, the solution could avoid machine-learning to detect expected crash conditions.

http://www.zubek.net/robert/publications/Needs-based-AI-draft.pdf

This should explain the context for what I mean by advertisements. My scoring system is a lot more complicated than the one described (Various Eigenvectors and what not), but it should give you a general idea.

In terms of speed I am currently getting, at the moment its not that bad. I can calculate 20,000 iterations a second for a system with 4.3k agents and 500 advertisements. However, for the game I'm currently working on, that number would most likely need to increase by a factor of 3 - 5. (I figured out that if I could just copy segments of the graph rather than recalculating, the factor increase would be nearly 10. That number is very attractive to me haha)