Genetic Programming and Neural Networks as Interpreters for a Distributive Tactile Sensing System

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "U. Pattananupong and P. Tongpadungrod and 
                 N. Chaiyaratana",
  title =        "Genetic Programming and Neural Networks as
                 Interpreters for a Distributive Tactile Sensing
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "4027--4034",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1231.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4424996",
  abstract =     "This paper describes performance of a neural network
                 and genetic programming (GP) in identifying the state
                 of contact in a distributive tactile sensing system.
                 The chosen architecture for the neural network is a
                 multilayer perceptron while that for the genetic
                 programming is a structured representation on genetic
                 algorithms for non-linear function fitting
                 (STROGANOFF). The tactile system comprises a small
                 matrix of sensors for detecting deformation of a
                 tactile surface. The determination of contact state is
                 completed using both simulated and experimental inputs.
                 Because the system relies on few sensing positions
                 hence a robust interpreting algorithm plays a vital
                 role. The study involves the identification of the
                 position of a pointed load for a range between 200-600
                 g which can be applied across the surface. The
                 performance in determining the position is described in
                 the form of absolute deviation from the actual applied
                 position. The simulation result indicates that the
                 multilayer perceptron is the best inference technique
                 while the GP-based mapping model produces a better
                 result in an experiment with a high load. The
                 difference between the simulation and the experiment is
                 the result of an inability of the simulation model at
                 capturing true plate deflection characteristics.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",

Genetic Programming entries for U Pattananupong P Tongpadungrod Nachol Chaiyaratana