On an interval type-2 TSK FLS A1-C1 consequent parameters tuning

In order to take into account the uncertainties about the antecedent membership functions and consequent parameter values in the type-1 TSK Fuzzy Logic System (FLS), this latter has been extended to its type-2 counterpart. The Type-2 TSK FLS contains three different architectures. One of these architectures which handles the uncertainties of the consequent parameter values, when we are interested in the defuzzified output, gives the same result as an equivalent Type-1 TSK FLS. In order to keep the type-2 nature of this architecture and to reduce the computation complexity at its design stage, we propose a new design approach in which the consequent parameters are tuned using multiple output training data that correspond to the same inputs. To show the effectiveness and the feasibility of the proposed approach, we use a time series prediction example as a use case.