Synchronization, Learning and Classification for a System of Kuramoto Models
No Thumbnail Available
Date
2024
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of Exeter
Abstract
Kuramoto systems or Kuramoto networks model the behaviour of large sets of coupled
oscillators. Arising initially in the context of systems of chemical and biological
oscillators, they now find applications in various areas of science, engineering and
medicine, including neuroscience. A key property of Kuramoto networks is their
synchronization behaviour: for a network with N oscillators, it is possible that all
N oscillators synchronize, that several clusters of synchronized oscillators emerge,
or that there is no synchronization of the oscillators. The behaviour is a function
of the network parameters, namely coupling strengths and natural frequencies for
the oscillators, as well as their initial conditions. In this thesis, we consider control
systems theory approaches for Kuramoto networks that focus on adaptive learning of
system parameters and phase tracking, observation-based classification of synchrony,
and a combination of both.
We first consider synchronization and learning approaches for pairs of Kuramoto
networks. One network plays the role of a training network, the other is a learning
network. We consider synchronization and system parameter learning based on
phase information. Our main result is an adaptive learning strategy that tunes the
system parameters of the learning oscillator – the Kuramoto coupling strengths and
the natural frequencies – to achieve phase tracking, i.e. synchronization, between
the training and learning phases. Tracking is proved using a Lyapunov stability
approach. The adaptive strategy also guarantees partial convergence of the learning
weights and frequencies to those of the training oscillator. Partial convergence is
characterized by the linear dependence of the phase differences of the states of the
training oscillator. The results are illustrated by a Kuramoto network with N = 4
oscillators.
Secondly, and generalizing the synchronization and learning result, we consider
networks where only output information is available and not all phases of the network
i
may be measured independently. A crucial aspect of this approach is the concept
of observability and observer design for dynamical systems, i.e. how to make use
of output information to recreate phase information. This is an unsolved problem
for Kuramoto networks where a training system is not necessarily in an equilibrium
state. To overcome this problem we develop a machine learning-based approach
using so-called “fingerprints” of the networks output signals, i.e. spectrogram images
that represent the possible synchronization behaviours. We use a simple artificial
neural network architecture to develop a pattern recognition tools that classifies the
“fingerprints” and thus the types synchrony as observed by outputs of Kuramoto
networks of a fixed size. The approach is illustrated by simulation and classification
results for Kuramoto networks with N = 4 and N = 7 oscillators. Using the
classifier approach we then develop a switched systems adaptive control framework
to determine the type of Kuramoto network responsible or able to create a given
“fingerprint” that matches the “fingerprint” of the training system. Our second main
result is an adaptive algorithm that can learn the behaviour of a Kuramoto network,
from a set or family of possible networks, to match the output-based “fingerprint” of
the training system. The results are illustrated for networks of N = 4 and N = 7
oscillators with a variety of synchrony outputs, respectively
Description
Kuramoto networks model the behavior of large ensembles of coupled oscillators.
Initially developed to describe systems of chemical and biological oscillators, they
now have applications across diverse fields, particularly in neuroscience. Through
mathematical analysis and simulation studies, the results from this thesis enrich the
existing understanding of synchronization dynamics, particularly in the context of
networks of Kuramoto oscillators. Specifically, this thesis contributes to the field
by focusing on tracking, learning, classification and recognition. First, we construct
parameter adaptation algorithms so that a learning network can track the phases and
learn the parameters of a training network. Next, we build a neural network based
system that can classify networks of Kuramoto oscillators based only on network
outputs and their fingerprints (in the form of a spectrogram). Finally, we create a
learning/recognition algorithm that can learn or find a Kuramoto oscillator to match
the ( fingerprint/output) of the training system. The focus has been on extracting
meaningful patterns and information from complex signals.
Keywords
Synchronization, Kuramoto model, Classification, Machain learning