Nonlinear model identification of an experimental ball-and-tube system using a genetic programming approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Leandro {dos Santos Coelho} and 
                 Marcelo Wicthoff Pessoa",
  title =        "Nonlinear model identification of an experimental
                 ball-and-tube system using a genetic programming
  journal =      "Mechanical Systems and Signal Processing",
  volume =       "23",
  number =       "5",
  pages =        "1434--1446",
  year =         "2009",
  ISSN =         "0888-3270",
  DOI =          "doi:10.1016/j.ymssp.2009.02.005",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, System
                 identification, Nonlinear models, Evolutionary
  abstract =     "Most processes in industry are characterized by
                 nonlinear and time-varying behavior. The identification
                 of mathematical models typically nonlinear systems is
                 vital in many fields of engineering. The developed
                 mathematical models can be used to study the behavior
                 of the underlying system as well as for supervision,
                 fault detection, prediction, estimation of unmeasurable
                 variables, optimization and model-based control
                 purposes. A variety of system identification techniques
                 are applied to the modeling of process dynamics.
                 Recently, the identification of nonlinear systems by
                 genetic programming (GP) approaches has been
                 successfully applied in many applications. GP is a
                 paradigm of evolutionary computation field based on a
                 structure description method that applies the
                 principles of natural evolution to optimization
                 problems and its nature is a generalized hierarchy
                 computer program description. GP adopts a tree
                 structure code to describe an identification problem.
                 Unlike the traditional approximation methods where the
                 structure of an approximate model is fixed, the
                 structure of the GP tree itself is modified and
                 optimized and, thus, there is a possibility that GP
                 trees could be more appropriate or accurate approximate
                 models. This paper focuses the GP method for structure
                 selection in a system identification applications. The
                 proposed GP method combines different techniques for
                 tuning of crossover and mutation probabilities with an
                 orthogonal least-squares (OLS) algorithm to estimate
                 the contribution of the branches of the tree to the
                 accuracy of the discrete polynomial Nonlinear
                 AutoRegressive with eXogenous inputs (NARX) model. The
                 nonlinear system identification procedure, based on a
                 NARX representation and GP, is applied to empirical
                 case study of an experimental ball-and-tube system. The
                 results demonstrate that the GP with OLS is a promising
                 technique for NARX modeling.",

Genetic Programming entries for Leandro dos Santos Coelho Marcelo Wicthoff Pessoa