Optimal Fuzzy Model Construction with Statistical Information using
  Genetic Algorithm
  Fuzzy rule based models have a capability to approximate any continuous
function to any degree of accuracy on a compact domain. The majority of FLC
design process relies on heuristic knowledge of experience operators. In order
to make the design process automatic we present a genetic approach to learn
fuzzy rules as well as membership function parameters. Moreover, several
statistical information criteria such as the Akaike information criterion
(AIC), the Bhansali-Downham information criterion (BDIC), and the
Schwarz-Rissanen information criterion (SRIC) are used to construct optimal
fuzzy models by reducing fuzzy rules. A genetic scheme is used to design
Takagi-Sugeno-Kang (TSK) model for identification of the antecedent rule
parameters and the identification of the consequent parameters. Computer
simulations are presented confirming the performance of the constructed fuzzy
logic controller.
