Supervised Chaotic Source Separation by a Tank of Water

TL;DR

This paper introduces a supervised learning framework that uses a tank of water as an intermediate dynamical system to separate mixed chaotic signals without prior knowledge of their equations, demonstrating effectiveness in complex scenarios.

Contribution

The paper presents a novel, generalizable method for separating nonlinear, chaotic sources using a physical dynamical system as an intermediate processor, bridging chaos theory and machine learning.

Findings

Successful separation of chaotic signals using a water tank system

Framework generalizes to various chaotic trajectories

Performance explained by state-observer theory

Abstract

Whether listening to overlapping conversations in a crowded room or recording the simultaneous electrical activity of millions of neurons, the natural world abounds with sparse measurements of complex overlapping signals that arise from dynamical processes. While tools that separate mixed signals into linear sources have proven necessary and useful, the underlying equational forms of most natural signals are unknown and nonlinear. Hence, there is a need for a framework that is general enough to extract sources without knowledge of their generating equations, and flexible enough to accommodate nonlinear, even chaotic, sources. Here we provide such a framework, where the sources are chaotic trajectories from independently evolving dynamical systems. We consider the mixture signal as the sum of two chaotic trajectories, and propose a supervised learning scheme that extracts the chaotic trajectories from their mixture. Specifically, we recruit a complex dynamical system as an intermediate processor that is constantly driven by the mixture. We then obtain the separated chaotic trajectories based on this intermediate system by training the proper output functions. To demonstrate the generalizability of this framework \emph{in silico}, we employ a tank of water as the intermediate system, and show its success in separating two-part mixtures of various chaotic trajectories. Finally, we relate the underlying mechanism of this method to the state-observer problem. This relation provides a quantitative theory that explains the performance of our method, such as why separation is difficult when two source signals are trajectories from the same chaotic system.