The paper, “Learning Mixtures of Linear Dynamical Systems,” uses a machine learning approach to understand a mixture of linear dynamical systems. These systems include a wide range of phenomena, such as viral disease outbreaks and weather patterns.

The research was led by Yanxi Chen, a Princeton ECE graduate student, and H. Vincent Poor, the Michael Henry Strater University Professor and professor of electrical and computer engineering.

Chen said they developed an algorithm to learn these dynamical systems and validated their theoretical studies with numerical experiments, thus showing that the algorithm is successful. They outlined how the algorithm works in the paper and showed how it is computationally and statistically efficient.

“It breaks new ground in being able to learn a mixture of dynamical systems,” said Poor. “It looks at the situation where you have a mixture of multiple dynamical processes going at the same time, with data coming from some or all of them, but you don’t know which are at work at a given time.”

Chen added, “You might have some measurements across a certain period of time, and the signal or data might be driven by a few different underlying physical laws or underlying models. And we don't know which model is driving which part of the signal. Our algorithm is able to understand and learn the different models in these complex systems.”

Applications for the algorithm could perhaps be deployed in robotics, driverless cars, healthcare settings and weather forecasting.

Poor noted that the conference had about 1,000 research papers presented. Twenty groups were awarded an outstanding paper award.