Soft computing for autonomous robotic systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Akbarzadeh-T:2000:CEE,
  author =       "M.-R. Akbarzadeh-T. and K. Kumbla and E. Tunstel and 
                 M. Jamshidi",
  title =        "Soft computing for autonomous robotic systems",
  journal =      "Computers and Electrical Engineering",
  volume =       "26",
  pages =        "5--32",
  year =         "2000",
  number =       "1",
  keywords =     "genetic algorithms, genetic programming, Soft
                 computing, Neural networks, Fuzzy logic, Robotic
                 control, Articial intelligence",
  URL =          "http://www.sciencedirect.com/science/article/B6V25-3Y6GXY5-2/1/6a6f9ff946815d4e95fe3884c98e74e5",
  URL =          "http://citeseer.ist.psu.edu/373353.html",
  size =         "28 pages",
  abstract =     "Neural networks (NN), genetic algorithms (GA), and
                 genetic programming (GP) are augmented with fuzzy
                 logic-based schemes to enhance artificial intelligence
                 of automated systems. Such hybrid combinations exhibit
                 added reasoning, adaptation, and learning ability. In
                 this expository article, three dominant hybrid
                 approaches to intelligent control are experimentally
                 applied to address various robotic control issues which
                 are currently under investigation at the NASA Center
                 for Autonomous Control Engineering. The hybrid
                 controllers consist of a hierarchical NN-fuzzy
                 controller applied to a direct drive motor, a GA-fuzzy
                 hierarchical controller applied to position control of
                 a flexible robot link, and a GP-fuzzy behavior based
                 controller applied to a mobile robot navigation task.
                 Various strong characteristics of each of these hybrid
                 combinations are discussed and used in these control
                 architectures. The NN-fuzzy architecture takes
                 advantage of NN for handling complex data patterns, the
                 GA-fuzzy architecture uses the ability of GA to
                 optimize parameters of membership functions for
                 improved system response, and the GP-fuzzy architecture
                 uses the symbolic manipulation capability of GP to
                 evolve fuzzy rule-sets.",
  notes =        "citeseer 373353 version not identical to published
                 version",
}

Genetic Programming entries for Mohammad-R Akbarzadeh-Totonchi K Kumbla Edward W Tunstel Mohammad Jamshidi

Citations