Genetic programming for model selection of TSK-fuzzy systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Hoffmann:2001:IS,
  author =       "Frank Hoffmann and Oliver Nelles",
  title =        "Genetic programming for model selection of TSK-fuzzy
                 systems",
  journal =      "Information Sciences",
  year =         "2001",
  volume =       "136",
  number =       "1-4",
  pages =        "7--28",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, Fuzzy
                 modeling, Neuro-fuzzy system",
  URL =          "http://www.sciencedirect.com/science/article/B6V0C-43DDW06-2/1/69cfc0ce8977ebea74cb8cec74efa722",
  URL =          "http://citeseer.ist.psu.edu/cache/papers/cs/22985/http:zSzzSzwww.nada.kth.sezSz~hoffmannzSzjis2001.pdf/genetic-programming-for-model.pdf",
  URL =          "http://citeseer.ist.psu.edu/459134.html",
  size =         "22 pages",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/S0020-0255(01)00139-6",
  abstract =     "This paper compares a genetic programming (GP)
                 approach with a greedy partition algorithm (LOLIMOT)
                 for structure identification of local linear
                 neuro-fuzzy models. The crisp linear conclusion part of
                 a Takagi-Sugeno-Kang (TSK) fuzzy rule describes the
                 underlying model in the local region specified in the
                 premise. The objective of structure identification is
                 to identify an optimal partition of the input space
                 into Gaussian, axis-orthogonal fuzzy sets. The linear
                 parameters in the rule consequent are then estimated by
                 means of a local weighted least-squares algorithm.
                 LOLIMOT is an incremental tree-construction algorithm
                 that partitions the input space by axis-orthogonal
                 splits. In each iteration it greedily adds the new
                 model that minimizes the classification error. GP
                 performs a global search for the optimal partition tree
                 and is therefore able to backtrack in case of
                 sub-optimal intermediate split decisions. We compare
                 the performance of both methods for function
                 approximation of a highly non-linear two-dimensional
                 test function and an engine characteristic map.",
}

Genetic Programming entries for Frank Hoffmann Oliver Nelles

Citations