Basic Models : Controlling Chaos - Weeks & Burgess

Burgess & Weeks (1997) show that chaotic systems can be stablised through evolving neural networks into feedback controllers using small perturbations.

First, chaotic systems are deterministic, meaning they obey some simple rules. In general this means that we can predict their behavior for short times. Second, chaotic systems have sensitive dependence on initial conditions, which means we can't predict their behavior for long times. The weather is an example of a system with these two traits: you can predict the weather five minutes from now with excellent success, the weather tomorrow is reasonably predictable, and the weather two weeks from now is a mystery. Third, chaotic systems generally have underlying patterns, sometimes called attractors. For the weather, the climate is in some sense an attractor. We can't predict the weather two weeks from now, but we can safely say Texas won't be covered with blizzards. Fourth, chaos is generally thought of as low-dimensional, meaning it can be described by a few simple variables (Weeks & Burgess)

Link to Burgess & Weeks