Genetic processing of the sensorial information. del Castillo Sobrino, D.; Casao; Gaśos, J.; Śanchez; and García-Alegre, C. Sensors and Actuators A: Physical, 37-38:255--259, 1993.
doi  abstract   bibtex   
A general method for optimizing the behavior of fuzzy control systems that receive information coming from a sensorial system is presented. This optimization is achieved by the genetic processing of the entry data to the system. Fuzzy control systems work with a set of fuzzy variables. The fuzzy membership functions that define these variables perform a kind of packing over the information coming from the sensors. These fuzzy membership functions have an unfixed shape and a set of unfixed anchor points that may be adjusted by several methods in order to obtain a good performance of the control system. Due to the trial and error method being time consuming, an alternative method, based on genetic algorithms, for adjusting these parameters is proposed. Genetic algorithms are a search technique analogous to natural genetics. Genetic information encoding and the implemented genetic algorithms are used to adjust the fuzzy membership functions associated with the linguistic labels that define the fuzzy variables of a rule-based control system. The designed control system allows the local navigation of a mobile autonomous robot avoiding unexpected obstacles in a partially unknown environment.
@article{ DelCastilloSobrino1993,
  title = {{Genetic processing of the sensorial information}},
  author = {{del Castillo Sobrino}, Dolores and Casao, Jorge Gaś{o}s and Ś{a}nchez, Carmen García-Alegre},
  journal = {Sensors and Actuators A: Physical},
  year = {1993},
  pages = {255--259},
  volume = {37-38},
  abstract = {A general method for optimizing the behavior of fuzzy control systems that receive information coming from a sensorial system is presented. This optimization is achieved by the genetic processing of the entry data to the system. Fuzzy control systems work with a set of fuzzy variables. The fuzzy membership functions that define these variables perform a kind of packing over the information coming from the sensors. These fuzzy membership functions have an unfixed shape and a set of unfixed anchor points that may be adjusted by several methods in order to obtain a good performance of the control system. Due to the trial and error method being time consuming, an alternative method, based on genetic algorithms, for adjusting these parameters is proposed. Genetic algorithms are a search technique analogous to natural genetics. Genetic information encoding and the implemented genetic algorithms are used to adjust the fuzzy membership functions associated with the linguistic labels that define the fuzzy variables of a rule-based control system. The designed control system allows the local navigation of a mobile autonomous robot avoiding unexpected obstacles in a partially unknown environment.},
  doi = {10.1016/0924-4247(93)80043-G},
  file = {::},
  issn = {09244247}
}
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