Dynamical system modelling using radial basis functions

Iain Mann, Steve McLaughlin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The problem of modelling complex, chaotic dynamical systems is considered. A radial basis function (RBF) neural network is used to learn the system dynamics, and is then operated in free-running made to generate time series which have very similar dynamical properties to those of the original training data. In our RBF network it is possible to fix the centre positions on a data-independent hyperlattice which implies that only the linear-in-the-parameters weights need to be learnt for each different systems. This leads to a compact, efficient structure which, with regularisation applied to the learning of the weights, produces a correct, stable output. The dynamics of the synthesised system are verified by examining both the correlation dimension and Lyapunov exponents.

Original languageEnglish
Title of host publicationIEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000
PublisherIEEE
Pages461-465
Number of pages5
ISBN (Print)0-7803-5800-7
DOIs
Publication statusPublished - 2000
EventIEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000 - Lake Louise
Duration: 1 Oct 20004 Oct 2000

Conference

ConferenceIEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000
Abbreviated titleAS-SPCC 2000
CityLake Louise
Period1/10/004/10/00

Cite this

Mann, I., & McLaughlin, S. (2000). Dynamical system modelling using radial basis functions. In IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000 (pp. 461-465). IEEE. https://doi.org/10.1109/ASSPCC.2000.882519