IDENTIFICATION OF POSSIBILISTIC LINEAR-SYSTEMS BY QUADRATIC MEMBERSHIP FUNCTIONS OF FUZZY PARAMETERS

We have already discussed several models of the possibilistic linear regression analysis under the assumption that possibilistic parameters in the models are non-interactive, i.e., the possibilistic distribution of parameters is defined by minimum operators. In this paper, quadratic membership functions as defined by A. Celmins are considered to propose an identification method of interactive fuzzy parameters in possibilistic linear systems. Our method can be reduced to linear programming, so that it is very easy to obtain the possibilistic distribution of parameters.