Functional reconstruction of dynamical systems from time series using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "T. McConaghy and H. Leung and V. Varadan",
  title =        "Functional reconstruction of dynamical systems from
                 time series using genetic programming",
  booktitle =    "26th Annual Conference of the IEEE Industrial
                 Electronics Society, IECON 2000",
  year =         "2000",
  volume =       "3",
  pages =        "2031--2034",
  address =      "Nagoya",
  month =        "22-28 " # oct,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/IECON.2000.972588",
  abstract =     "Reconstruction of a chaotic system from its
                 measurement is a challenging problem. It requires the
                 determination of an embedding dimension and a nonlinear
                 mapping that approximates the underlying unknown
                 dynamics. We propose the use of genetic programming
                 (GP) to find the exact functional form and embedding
                 dimension of an unknown dynamical system automatically.
                 Using functional operators of addition, multiplication,
                 and time-delay, with the least-squares estimation
                 technique, we use GP to reconstruct the exact chaotic
                 polynomial system and its embedding dimension from a
                 time series. If the underlying dynamic does not come
                 from a polynomial system, the proposed GP method will
                 produce an optimal polynomial predictor for the time
                 series. Simulations showed that the GP approach
                 outperformed a radial basis function neural network in
                 predicting both polynomial and nonpolynomial chaotic

Genetic Programming entries for Trent McConaghy Henry Leung Vinay Varadan