Jeremy P. Huke

Delay Embeddings for Forced Systems.II. Stochastic Forcing

Abstract Takens’ Embedding Theorem forms the basis of virtually all approaches to the analysis of time series generated by nonlinear deterministic dynamical systems. It typically allows us to reconstruct an unknown dynamical system which gave rise to a given observed scalar time series simply by constructing a new state space out of successive values of the time series. This provides the theoretical foundation for many popular techniques, including those for the measurement of fractal dimensions and Liapunov exponents, for the prediction of future behaviour, for noise reduction and signal separation, and most recently for control and targeting. Current versions of Takens’ Theorem assume that the underlying system is autonomous (and noise-free). Unfortunately this is not the case for many real systems. In a previous paper, one of us showed how to extend Takens’ Theorem to deterministically forced systems. Here, we use similar techniques to prove a number of delay embedding theorems for arbitrarily and stochastically forced systems. As a special case, we obtain embedding results for Iterated Functions Systems, and we also briefly consider noisy observations.

Scaling and interleaving of subsystem Lyapunov exponents for spatio-temporal systems.

The computation of the entire Lyapunov spectrum for extended dynamical systems is a very time consuming task. If the system is in a chaotic spatio-temporal regime it is possible to approximately reconstruct the Lyapunov spectrum from the spectrum of a subsystem by a suitable rescaling in a very cost effective way. We compute the Lyapunov spectrum for the subsystem by truncating the original Jacobian without modifying the original dynamics and thus taking into account only a portion of the information of the entire system. In doing so we notice that the Lyapunov spectra for consecutive subsystem sizes are interleaved and we discuss the possible ways in which this may arise. We also present a new rescaling method, which gives a significantly better fit to the original Lyapunov spectrum. We evaluate the performance of our rescaling method by comparing it to the conventional rescaling (dividing by the relative subsystem volume) for one- and two-dimensional lattices in spatio-temporal chaotic regimes. Finally, we use the new rescaling to approximate quantities derived from the Lyapunov spectrum (largest Lyapunov exponent, Lyapunov dimension, and Kolmogorov-Sinai entropy), finding better convergence as the subsystem size is increased than with conventional rescaling. (c) 1999 American Institute of Physics.

Delay embedding in the presence of dynamical noise

We present a new embedding theorem for time series, in the spirit of Takenss theorem, but requiring multivariate signals. Our result is part of a growing body of work that extends the domain of geometric time series analysis to some genuinely stochastic systems-including such natural examples where φ is some fixed map and the ηi are i.i.d. random displacements

Topology from time series

We describe methods for the study of topological properties of the invariant manifolds of experimental dynamical systems. We explain how to compute such invariants as the Euler characteristic and Betti numbers using time series data, and suggest a number of potential applications.

Iterated function system models of digital channels

This paper introduces a new class of models of digital communications channels. Physically, these models take account of the digital nature of the input. Mathematically, they are iterated function systems. As a consequence of making explicit assumptions about the role of discreteness in the models, it is possible to make general statements about the behaviour of these channels without needing to assume that they are linear. We provide the mathematical background necessary to understand the behaviour of these models and prove a number of results about their observability. We also provide a number of examples intended to demonstrate their connection with linear state–space models, and to suggest how the nonlinear theory might be developed towards applications.

Finite-dimensional behaviour and observability in a randomly forced PDE

In earlier work [D.S. Broomhead, J.P. Huke, M.R. Muldoon, and J. Stark, Iterated function system models of digital channels, Proc. R. Soc. Lond. A 460 (2004), pp. 3123–3142], aimed at developing an approach to signal processing that can be applied as well to nonlinear systems as linear ones, we produced mathematical models of digital communications channels that took the form of iterated function systems (IFS). For finite-dimensional systems these models have observability properties indicating they could be used for signal processing applications. Here we see how far the same approach can be taken towards the modelling of an infinite-dimensional system. The cable equation is a well-known partial differential equation (PDE) model of an imperfectly insulated uniform conductor, coupled to its surroundings by capacitive effects. (It is also much used as a basic model in theoretical neurobiology.) In this article we study the dynamics of this system when it is subjected to randomly selected discrete input pulses. The resulting IFS has a unique finite-dimensional attractor; we use results of Falconer and Solomyak to investigate the dimension of this attractor, relating it to the physical parameters of the system. Using work of Robinson, we show how some of the observability properties of the IFS model are retained.