Particle swarm optimization is often used to optimize functions in rather unfriendly non-convex, non-continuous spaces. The idea behind the algorithm involves a swarm of particles flying through a space both collaboratively and independently.
Python code is shown that estimates the Lyapunov spectra for the Rossler and Lorenz systems. This code is written in a way that makes it adaptable for other continuous-time systems.
Support Vector Regression is a technique in machine learning that can be used to model chaotic data. A program is shown to work on Delayed Henon Map data.
